United States
Environmental Protection
Agency
Environmental Research
Laboratory
Athens GA 30613
EPA-600/3-84-109
December 1984
Research and Development
xvEPA
Users Manual for the
Pesticide Root Zone
Model (PRZM),
Release 1
-------
EPA-600/3-84-109
December 1984
USERS MANUAL FOR THE PESTICIDE ROOT ZONE MODEL (PRZM)
Release 1
by
Robert F. Carsel, Charles N. Smith, Lee A. Mulkey
J. David Dean''', and Peter Jowiset
Technology Development and Applications Branch
Environmental Research Laboratory
Athens, Georgia 30613
'''Anderson Nichols, Inc.
2666 East Bayshore Road
Palo Alto, California 94549
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 30613
-------
DISCLAIMER
Itie information in this documsnt has been funded by the United States
Environmental Protection Agency. It has been subject to the Agency's peer
and administrative review, and it has been approved as an EPA document.
Mention of trade names or commercial products does not constitute endorsement
or recommendation for use by the U.S. Environmental Protection Agency.
11
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FOREWORD
As environmental controls become more costly to implement and the
penalties of judgement errors become more severe, environmental quality
management requires more efficient management tools based on greater
knowledge of the environemental phenomena to be managed. As part of this
laboratory's research on the occurrence, movement, transformation, impact,
and control of environmental contaminants, the Technology Development and
Applications Branch develops management and engineering tools to help
pollution control officials achieve water quality goals through watershed
management.
Groundwater contamination by leaching of pesticides is a recognized
environmental problem. As an aid to environmental decision-makers, the
Pesticide Root Zone Model (PRZM) was developed to predict the movement of
pesticides within and below the plant root zone to assess subsequent threats
of contaminating groundwater.
The manual is intended to assist the model user in developing logical,
well-defined, and well-documented technical evaluations that can provide:
• frequency distributions of leaching potential that may be
used in risk assessment;
• guidance for monitoring compliance with conditional
registrations;
• information for selecting alternative land management practices
to reduce leaching such as applying pesticides in alternate
years, timing of application, reducing application rates, and
splitting applications,- and
• leaching potential for new chemicals .
Rosemarie C. Russo, Ph.D.
Director
Environmental Research Laboratory
Athens, Georgia
iii
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ABSTRACT
The Pesticide Root Zone Model (PRZM) simulates the vertical movement
of pesticides in the unsaturated soil, within and below the plant root
zone, and extending to the water table using generally available input data
that are reasonable in spatial and temporal requirements. The model consists
of hydrology and chemical transport components that simulate runoff, erosion,
plant uptake, leaching, decay, foliar washoff and volatilization (implicitly)
of a pesticide. Predictions can be made for daily, monthly or annual
output. It is designed to run on a DEC POP 1170 mini-computer using batch
jobstream submission. With modifications, however, the model will operate
on other computers with FORTRAN compilers.
PRZM has a separate interactive processing software module (ANPRZM) to
develop and update parameter files for calibration, verification, and
production run analyses. The model has undergone limited performance
testing in New York and Wisconsin (potatoes), Florida (citrus) and Georgia
(corn) (7), (24-25). The results of these tests demonstrate that PRZM is a
useful tool for evaluating groundwater threats from pesticide use.
The manual provides information and detailed guidance on parameter
estimation and model operation as well as an example application to assist
model users.
This report covers a period from January 1, 1982 to October 1, 1984,
and work was completed as of April 1, 1984.
IV
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CONTENTS
Foreword 11:L
Abstract ±v
Figures vii
Tables viii
Acknowledgments x
SECTION 1 - INTRODUCTION
1 .1 Background 1
1.2 Exposure Assessment 1
1 .3 Overview of Manual 4
1 .4 Key Bibliography for User 5
SECTION 2 - THEORY
2.1 Introduction 6
2.2 Description of Basic Transport Equations 7
2.3 Application of Theory in PRZM 13
2.4 Water Balance Equations . 14
2.5 Erosion Equations 19
2.6 Chemical Transport Equations 20
SECTION 3 - MODEL STRUCTURE AND DATA INPUT
3.1 Introduction 21
3.2 PRZM Structure 21
3.3 Data Input 25
SECTION 4 - PARAMETER ESTIMATION
4.1 Introduction 39
4.2 Hydrology 39
4.2.1 Snow Factor and Pan Factor 39
4.2.2 Soil Evaporation Moisture Loss during Fallow, Dormant
Periods 41
4.2.3 Average Day Time Hours for a Day in Each Month ... 41
4.2.4 Soil Erosion 41
4.2.5 Maximum Crop Interception 47
4.2.6 Active Crop Rooting Depth 47
4.2.7 Runoff and Infiltration 47
4.2.8 Maximum Areal Coverage 61
4.2.9 Maximum Foliar Dry Weight 61
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CONTENTS (Continued)
4.2.9.1
4.3
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
4.3.6
4.3.7
4.3.8
4.3.9
4.3.9.1
4.4
4.4.1
4.4.2
4.4.3
4.4.4
4.4.5
SECTION 5 -
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
SECTION 6 -
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
Cropping Information for Emergence, Maturity, and
Harvest , .
Initial Foliage to Soil Distribution . .
Foliar Washoff Flux , .
Pesticides Soil-Water Distribution Coefficients . .
Options for Use in Estimating Distribution
Coefficients from Related Water Solubility Data . .
Degradation Rate Constants in Soil Root Zone . . .
Dispersion
Pesticide Application , .
Soil Moisture Estimation Technique Problems ....
OPERATIONAL MODELING CONSIDERATIONS
ANPRZM: A Pre-Processing Module for Interactive
Auxiliary Information
SIMULATION STRATEGY
Introduction
Primary Intent Description/Operational Learning
General Calibration and Exposure Assessment ....
Exposure Assessment, Sensitivity Analysis and
Documentation and Reporting of Results
62
63
63
63
63
63
65
71
71
74
75
79
79
80
86
88
89
89
92
92
92
94
96
96
100
103
105
106
107
107
116
1 17
1 18
123
VI
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CONTENTS (Continued)
SECTION 7 - APPENDICES
A PRZM Developmental References 125
B Soil Names and Hydrologic Classifications 130
C Example Data Sets 181
D Julian Day Calendar 188
E Programmer's Guide 189
FIGURES
Number Page
1 Pesticide Root Zone Model 2
2 Compartmental model for pesticide transport in soil 8
3 Generalized flow chart of Pesticide Root Zone Model 22
4 Pan evaporation correction factors 40
5 Diagram for estimating soil evaporation loss 42
6 Diagram for estimating storm duration intervals 45
7 Diagram for estimating SCS soil hydrologic groups 59
8 Numerical dispersion associated with space step 77
9 Physical dispersion associated with advective transport .... 78
10 1/3-Bar soil moisture by volume 83
11 15-Bar soil moisture by volume 83
1 2 Mineral bulk density 87
13 Estimation of drainage rate versus number of compartments ... 91
14 Example sensitivity testing scheme for KS 119
15 Cumulative frequency distribution of pesticide
leaving root zone 122
16 Documentation data sheet for PRZM assessment of
the unsaturated zone 124
E-1 PRZM subroutine structure 190
vii
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TABLES
Number Page
1 Variable designations for plotting files , . 35
1A Conversion factors for English and metric units . . 37
2 Actual daytime hours for latitudes 24-54° north of the equator . 43
3 Indications of the general magnitude of.the.soil/erodibility
4
5
6
7
8
9
1 0
1 1
12
13
14
15
Values of the erosion equation's topographic factor, LS,
for specified combinations of slope length and steepness . „ .
Generalized values of the cover and management factor, C, in
Agronomic data for major agricultural crops in the
United States .
Runoff curve numbers for hydrologic soil-cover complexes ....
Reduction in runoff curve numbers caused by conservation
Values for estimating WFMAX in exponential foliar model. ....
Degradation rate constants of selected pesticides on foliage ., .
Physical characteristics of selected pesticides for use in
48
49
50
53
54
56
57
57
58
62
64
development of partition coefficients (using water solubility)
and reported degradation rate constants in soil root zone . . 66
16 Octanol water distribution coefficients and soil
degradation rate constants for selected pesticides . 72
viii
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TABLES (Continued)
Number Page
17 Pesticide soil application methods and distribution 80
18 Coefficients for linear regression equations for prediction of
soil water contents at specific matric potentials 84
19 Hydrologic properties by soil texture 85
20 Mean bulk density for five soil textural classifications .... 88
21 Hydrologic properties by soil texture 90
22 Selected examples of observed seasonal evapotranspiration for
well-watered, common crops in U.S.A 101
23 Soil properties for Norfolk sandy loam 108
24 Tillage operations for continuous peanuts 108
25 PRZM sensitivity testing parameters 118
E-1 PRZM program variables, units, location, and variable
designation 192
E-2 PRZM fatal error messages and appropriate user actions 216
IX
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ACKNOWLEDGEMENTS
The authors would like to express their sincere appreciation to
Dr. Richard Green, Soil Physicist, University of Hawaii, Honolulu, HI,
for his helpful guidance and suggestions.
Acknowledgement is also made to Annie Smith and Sandra Ashe of the
Environmental Research Laboratory, Athens, Georgia, for their patience
in typing the many drafts of this manuscript. Without their professional
capability our goal of obtaining a user's manual for PRZM would not have
been obtainable.
A special acknowledgement to Tom Prather, Bruce Bartell, Ronnie Moon,
Susan Sims, and Taube Wilson of the Computer Sciences Corporation for their
helpful suggestions on ADP formatting and patience in drafting the many
figures for this guide.
We thank Jack Kittle of Anderson Nichols, Inc., for his many helpful
suggestions and programming expertise.
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SECTION 1
INTRODUCTION
1 .1 BACKGROUND
The Pesticide Root Zone Model (PRZM) is a dynamic, compartmental
model for use in simulating chemical movement in the unsaturated soil
systems within and below the plant root zone (see Figure 1). Time-
varying transport, including advection, and dispersion are represented
in the program. PRZM has two major components: hydrology and
chemical transport. The hydrology component for calculating runoff
and erosion is based on the Soil Conservation Service curve number
technique and the universal soil loss equation. Evapotranspiration
is estimated from pan evaporation data or by an empirical formula if
input pan data are unavailable. Evapotranspiration is divided among
evaporation from crop interception, evaporation from soil, and
transpiration from the crop. Water movement is simulated by the use
of generalized soil terms including field capacity, wilting point,
and saturation. Drainage from loose, porous and tighter compact
soils is simulated. To produce soil water and solid phase concentra-
tions, the chemical transport component calculates pesticide uptake
by plants, surface runoff, erosion, decay, vertical movement, foliar
loss, dispersion, and retardation. A finite difference numerical
solution, using a backwards difference implicit scheme, is employed.
PRZM allows the user to perform dynamic simulations of potentially
toxic chemicals, particularly pesticides, that are applied to the soil
or to plant foliage. Dynamic simulations allow the consideration of
pulse loads, the prediction of peak events, and the estimation of
time-varying mass emission or concentration profiles, thus overcoming
limitations of the more commonly used steady-state models.
1.2 EXPOSURE ASSESSMENT
Evidence of potentially toxic pesticides in groundwater has led
to intensive efforts toward environmental risk assessment for existing
or new chemicals. The concept of risk reflects the probability of
causing an effect and implies that the organism must first have been
exposed to the pesticide for sufficient time and intensity to inflict
damage. The use of continuous simulation models to generate time
series data to derive probability statements about hydrologic events
-------
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is an accepted technique. Simulation models have been used to
estimate probabilities of environmental exposure expressed as
cumulative frequency distributions in surface waters.
Frequency distributions of the mass of pesticides leaching from
the plant root zone appear to be a valuable tool to assist in assigning
risk to pesticide use. For example, investigations of the frequency
of a specific quantity, say 10% of the amount applied, of pesticides
leaching below the root zone during any one year over a 20-year
period can be accomplished. A cumulative frequency distribution can
be related to the expected return interval for different mass emissions.
The return interval, then, can be related to risk information and a
statement of risk can be determined. The study of this exposure, or
exposure assessment, is defined as the evaluation of the mass (or
concentration) of pesticides released into or through environmental
compartments.
The source of pesticides in groundwater can arise from both
nonpoint sources (agricultural use) and point sources (disposal,
etc.). Nonpoint source contaminations are characterized by highly
variable loadings with rainfall events dominating the timing and
magnitude of the loading of pesticides leaching below the root zone.
Point sources are much less varied and the loadings are thought to
be steady inputs to the groundwater. Clearly, exposure assessment
in the unsaturated zone must accommodate both the nonpoint and point
source loading to groundwater.
Pesticide leaching from agricultural fields as nonpoint source
loads can lead to groundwater contamination. The potentially
widespread, areal nature of resulting contamination make remedial
actions difficult because there is no single plume eminating from a
"point source" ( the more common groundwater problem) that can be
isolated and controlled. In any case a more prudent approach to
prevention or reduction of pesticide groundwater contamination must
be based on understanding the relationships among chemical properties,
soil system properties, and the climatic and agronomic variables that
combine to induce leaching. Knowledge of these relationships can
allow £_ priori investigation of conditions that lead to problems and
appropriate actions can be taken to prevent widespread contamination.
Evaluation or screening process models should conform to the
maximum possible extent to known theory but must be structured to
enable efficient analysis of field situations with minimal require-
ments for specialized field data. In short, the goal is to integrate
the essential chemical-specific processes for leaching with reasonable
estimates of water movement through soil systems. Data input require-
ments are to be reasonable in spatial and temporal requirements and
generally available from existing data bases.
By use of modeling techniques the user can produce a time-series
of chemical mass or concentration loadings that reflect daily changes
-------
in precipitation, evapotranspiration, cropping practices, land
management activities, and application timing.
1.3 OVERVIEW OF MANUAL
This manual describes a mathematical simulation model that has
been developed, and partially tested, to evaluate pesticide leaching
potentials under field crop conditions. Considerable emphasis has
been placed on the development of a user oriented manual providing
observed data or estimation techniques for each model parameter.
Therefore, data are provided throughout the manual in tables, figures
and appendices. In cases where the user has site-specific data, these
should be used.
The user is responsible for evaluating whether the model is
appropriate for the intended use, the types of data required, model
parameter data and what analyses are to be accomplished with the
generated time-series data.
Following the introduction is a section on modeling capability
and theory. Provided is a summary of the equations solved in PRZM,
where they can be found in the program, and short discussions of the
transport and transformation processes included in the model.
Section 3 is an overview of the modularization of PRZM, the sub-
routines contained within the program, and their function within the
model framework. A description of each required "card group" (a
series of data records on computer file) labeled (1 -21) is provided
for preparation of data input files.
Section 4 details the estimation techniques and provides example
calculations for many of PRZM's parameters. The sequence followed
is in the same order as detailed in Section 3.
Section 5 discusses operational considerations of how to acquire
PRZM, machine limitations, installation on a computer and a descrip-
tion of the pre-processing program ANPRZM.
Section 6 details an example exercise in simulation strategy for
PRZM and provides a technique for groundwater threat assessment using
the cumulative frequency distribution to express the probability of
exposure.
The first appendix (A) is a listing of all references used in
building PRZM and provides a good source of information on modeling
pesticides and groundwater contamination.
The second appendix (B) is a listing of values for the hydrologic
soil-cover complex of tabulated soils. These values are used in
assigning curve numbers for use in the simulation. Approximately
10,000 soils are tabulated.
-------
The third appendix (C) provides example data sets including the
limited testing sets used in Long Island, New York, and Watkinsville,
Georgia; sample output is provided for demonstration from the Watkins-
ville data set.
The fourth appendix (D) provides a Julian day calendar for con-
verting Julian days (utilized by many modelers) to day/month combina-
tions as used in PRZM.
The fifth appendix (E) provides a listing of program variables,
their definition, and unit association. A supplemental programmer's
guide is discussed and provides guidance on modifying PRZM's FORTRAN
code.
A note on FORTRAN variables and units may be helpful. PRZM uses
metric units in its calculations; the unit area simulated is one hectare,
1 .4 KEY BIBLIOGRAPHY FOR USER
This manual is not intended to provide a tutorial on simulation
modeling. Several sources of excellent references are provided in
appendix one. The following bibliography will provde an essential
background for the inexperienced user.
Burkhead, B.E., Max, R.C., Karnes, R.B., and Neid, E. Usual
Planting and Harvesting Dates. USDA, Agricultural Handbook
No. 283. 1972.
Haan, C.T., Johnson, H.P., Brakensiek, D.L. (Eds.). Hydrologic
Modeling of Small Watersheds. American Society for Agricultural
Engineers. Michigan. 1982.
Knisel, Walter G., editor. CREAMS: A Field-Scale Model for
Chemicals, Runoff, and Erosion from Agricultural Management
Systems. USDA, Conservation Research Report No. 26, 640 pp.,
illus. 1980.
Linsley, R.K., Jr., Kohler, M.A., and Paulhus, J.L.H.
Hydrology for Engineers. McGraw-Hill. New York. 1975.
Smith, C.N., Leonard, R.A., Langdale, G.W., and Bailey, G.W.
Transport of Agricultural Chemicals from Small Upland Piedmont
Watersheds. U.S. Environmental Protection Agency, Athens,
Georgia. EPA-600/3-78-056. 1978.
Soil Conservation Service, USDA. SCS National Engineering
Handbook, Section 4, Hydrology. 1971.
Stewart, B.A., Woolhiser, D.A., Wishmeir, W.H., Caro, J.H., and
Fere, M. H. Control of Water Pollution from Cropland: Vol. I.
A Manual for Guideline Development. U.S. Environmental Protec-
tion Agency, Athens, Georgia. EPA-600/275-026a. 1975.
-------
SECTION 2
THEORY
2 .1 INTRODUCTION
Many investigators have studied the factors contributing to pesti-
cide leaching (4, 5, 10, 14, 37). These investigations have shown
that chemical solubility in water, sorptive properties, formulation,
and soil persistence determine the susceptibility of pesticides to
leach through soil. Similarly, the important environmental and agrono-
mic factors include soil properties, climatic conditions, crop type,
and cropping practices. In short, the hydrologic cycle interacts with
the chemical properties and characteristics to transform and transport
pesticides within and out of the soil profile. Vertical movement out
of and below the root zone can result in groundwater contamination and
is the problem for which the model to be discussed in this manual is
designed to investigate.
Modeling solute transport in porous media including soil systems
is not new. Numerical models have been developed for the movement of
solutes in soil columns for steady-state, transient, homogenous, and
multi-layered conditions (10, 14, 17). Included in such studies have
been linear and nonlinear sorption, ion exchange, and other chemical-
specific reactions. These investigations have proven valuable in
interpreting laboratory data, investigating basic transport processes,
and identifying controlling factors in transport and transformation.
As noted in a recent review of models for simulating the movement of
contaminants through groundwater flow systems, however, the successful
use of such models will require a great number and variety of detailed
field data (2). This unfortunate conclusion arises from the scaling
problems associated with laboratory experiments and the traditional
solution of the appropriate partial differential equations at points
or nodes in a finite-difference or finite-element grid network. Each
spatial segment modeled must be properly characterized—a most expen-
sive if not impossible, task for many modeling problems.
Such real problems in modeling pesticide leaching with existing
procedures are discouraging when one considers the need to evaluate
future problems arising from pesticides not yet widely distributed or
used. Evaluation or screening process models should conform to the
maximum possible extent to known theory but must be structured to
enable efficient analysis of field situations with minimal requirements
for specialized field data. In short, the goal is to integrate the
-------
essential chemical-specific processes for leaching with reasonable
estimates of water movement through soil systems. Data input require-
ments must be reasonable in spatial and temporal requirements and
generally available from existing data bases.
2.2 DESCRIPTION OF BASIC TRANSPORT EQUATIONS
The PRZM model is derived from the conceptual, compartmentalized
representation of the soil profile as shown in Figure 2. From consi-
deration of Figure 2 it is possible to write mass balance equations
for both the surface zone and the subsurface zones. For the surface
zone we can write
AAX 9(CW9)
= -JD-Jv-J]ya-Ju-jQ-R-Jf£>s+JDES+Jfi:pp+JFOF (1)
3t
AAX 3(Csps)
at
"~ ~JDS ~ JER ~ JDES + JADS
where A = cross-sectional area of soil column, L2
AX = depth dimension of compartment, L
GW = dissolved concentration of pesticide, ML
Cg = sorbed concentration of pesticide, MM
9 = volumetric water content of soil,
p = soil bulk density, ML
t = time, T
JD = mass rate of change by dispersion, MT
Jv = mass rate of change by advection, MT
= mass rate of change by transformation of dissolved phase,
MT~1
Ju = mass rate of change by plant uptake of dissolved phase,
MT~1
JQR = mass rate of change by removal in runoff, MT~1
= mass rate of change by pesticide application, MT
= mass rate of change by washoff from plants to soil, MT
JDS = mass rate of change by transformation of sorbed phase,
JER = mass rate of change by removal on eroded recliments, MT~1
'"'ADS = mass rate of change by adsorption, MT
JDES = mass rate of change by desorption, MT~1
Note that, if the kinetic representation of sorption and desorption are
equated, we can also write
DES = ADS
and the instantaneous equilibrium assumption results.
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Equations for the subsurface zones are identical to equations
1 and 2 except that JOR^ ^FOF' an<^ ^ER are dropped. J&pp applies
to subsurface zones only when pesticides are incorporated into the
soil.
Each term in equations 1 and 2 must now be further defined. Dis-
persion and diffusion are combined and are described using Pick's law
as
D a2cw9
where D = diffusion-dispersion coefficient, assumed constant,
cm2 day"1
C,T = dissolved concentration of pesticide, g cm
O *!>
9 = volumetric soil water content, citH cm~J
x = soil depth dimension, cm
AX = depth of soil, cm
A = cross-sectional area of soil column, cm2
The advective term, Jy, describes the movement of pesticide in the
bulk flow field and is written as
AAX9(CW9V)
Jv = ---------- (5)
8*
where V = velocity of water movement, cm day"1
Degradation of a pesticide in or on soil may be due to such processes
as hydrolysis, photolysis and microbial decay. If these proceses follow
pseudo first-order kinetics, the rate coefficients may be combined into
one decay coefficient. Assuming the same rate constants for both phases,
the rate of change of chemical out of the sorbed and dissolved pools
due to decomposition may be written as:
JDW = Kscwe AAX (6)
JDS = KscsPs AA* (7)
where K = lumped, first order rate constant, day
p = soil bulk density, g cm
C = sorbed concentration of pesticide, g gm~1
Plant uptake of pesticides is modeled by assuming that uptake of a
pesticide by a plant is directly related to transpiration rate. If the
chemical is passively carried by transpired water, then uptake is
given by:
-------
Ju = f Cw e e MX (8)
where J = uptake of pesticide (g cm day" )
f = the fraction of total water in the zone used for
evapotranspiration (day"1 )
e = an uptake efficiency factor (dimensionless )
Erosion and runoff losses as well as inputs to the surface zone
from foliar washoff are considered in the surface layer. The loss of
pesticide due to runoff is
Q
Jqr = --- cw A O)
aw
J = pesticide loss due to runoff (g day )
in which
Q = the daily runoff depth (cm day"1 )
and the loss of pesticide due to erosion is
3 Xe rom Kd Cw A
Jer = ---------------- (10)
in which
J = the pesticide loss due to erosion (g cm day )
Xe = the erosion sediment loss (tonnes day )
rom = the enrichment ratio for organic matter (g g~1 )
KJ = the adsorption partition coefficient (cm g )
A^ = watershed area (cm )
a = a units conversion factor
Pesticides can be applied to either bare soil if pre-plant condi
tions prevail or to a full or developing crop canopy if post-plant
treatments are desired. The pesticide application, J^pp, is a simple
input rate but must be partitioned between the plant canopy and the
soil surface. Two options are implemented in PRZM. The first simply
partitions the application proportional to the ground surface covered
by the plant canopy. The second approach defines the fraction, F, of
the application intercepted by the plant as
F = 1 - exp(-u WQ) (11)
where u = a filtration parameter (m2 kg~1 )
Q
W =• herbage areal density on a dry weight basis (kg m )
10
-------
Pesticides applied to the plant canopy can be transported to the
soil surface as a result of rainfall washoff. This term, Jpop, is
defined as
JFOF = E pr M A (12)
where E = extraction coefficient (cm~1)
P = daily rainfall depth (cm day )
M = mass of the pesticide on the plant surface (g cm~2)
per cross-sectional area
The foliar pesticide mass, M, is further subject to degradation and
volatilization and its rate of change is given by
AdM
= -KfMA - JFOF + Apb (13)
dt
where K^ = lumped first-order foliar rate constant (day )
Ap = application rate to the plant (g ha )
b = a unit's conversion factor
Adsorption and desorption in equations 6 and 7 are treated as
separate, kinetic processes. A convenient simplification is to assume
each process is rapid and reduce this process to the expression
Cs = Kd Cw (14)
Equation 14 is tantamount to a linear, instantaneous, and reversible
equilibrium condition in the soil-water matrix. Equation 14 also offers
the convenient means to combine equations 1 and 2 into a single
expression written in terms of the dissolved pesticide concentration
as follows:
9[Cw(e+KDps)] 32(CW9) (CW9V)
3t 9x2 9x
Q aXeromKD
-cw [Ks(e+KDps)+fee + — f ]
Ax Aw Ax
JAPP <1-F>
[ + FEPrM] (15)
Ax A
11
-------
Equation 15 is solved in PRZM for the surface layer with fee = 0;
a V •»•• v
for subsurface layers within the root zone with Q , e om D,
Ax Aw Ax
(except for the soil incorporated case), EPrM = 0; and the same for
subsurface layers below the root zone with the addition of fQ£ =0.
Equation 15 is a variation on the advection-dispersion model most
often derived as the basis for groundwater quality models. The plant
uptake term, represented here as a simple linear function of plant
transpiration, is not included in most representations and the runoff
and erosion terms are rarely included. Equation 15 could be modified
further to include the influence of a vapor phase pesticide component
following the approach of Jury et al. (26) . For many very soluble
pesticides, however, volatilization from within the soil profile is
not a major mode of loss (5).
Most solutions to Equation 15 (without runoff, erosion, and
uptake included) have been numerical because the velocity, v, and soil
moisture content, 9, are both functions of time, t, and distance, x.
Assumptions of constant velocity and moisture content have been made
by some investigators to enable development of analytical solutions.
Specifically, for pesticides Enfield and Carsel (13) have obtained
such a solution when v is both constant and known. For other situa-
tions, analytical solutions remain untractable and in any case v and
9 must be known or modeled.
Because v and 9 are not generally known and not generally measured
as part of routine monitoring programs, it is necessary to develop
additional equations for these variables. In the general case Darcy's
law can be combined with the continuity equation to yield
39 3 (30)
_- = — [k ----- ] (16)
3t 3x 3x
where k = hydraulic conductivity
0 = hydraulic potential
30
v = -k -- (17)
3x
For the general case, Equations 15-17 must be solved as coupled
equations. However, when the solute concentrations are quite low and
do not influence flow then the solute transport and flow equations can
be decoupled.
Equation 15 can be solved numerically. Gureghian et al. (17)
obtained such a solution and performed sensitivity analyses that can
give valuable insight into the leaching process. In their study, the
12
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movement of nitrogen in soil columns was investigated under transient,
steady-state, and multi-layered soil conditions. Earlier Davidson et
al. (10) developed numerical models for simultaneous water and solute
movement in soil profiles. Pesticides were used as solutes in the
study and different adsorption models were investigated for their
utility in explaining the observed concentration profiles. More re-
cently Enfield et al. (14) obtained steady-state solutions for Equation
15 for specified values of flow velocities and flow volumes (recharge).
Lacking in all these approaches is accommodation of the infiltration
processes at the soil surface, removal of soil moisture by evapotrans-
poration, plant uptake, variable chemical application times and amounts,
and mass balance accounting for runoff losses.
Inclusion of all the above processes in the simultaneous solution
of Equations 15-17 is difficult for several reasons. First, the equa-
tions are written for vertical movement at a point, but field soils
are quite variable and conditions at one point may not be representa-
tive of other points. This spatial variation is mathematically treated
by solving the equations in two or three dimensions but the problem
remains to characterize the physical system being modeled. Second,
the vertical characteristics of field soils are also highly variable
and inputs required for Equations 15-17 may not be within economical
reach. In addition, the hydraulic conductivity and pressure head
versus moisture content relationships of some soils may not be single-
valued functions. Indeed, in a recent review of groundwater modeling,
Anderson (2) noted that the scarcity of field data and the lack of
appropriate measurement techniques remain as obstacles to routine
application of models (advection-dispersion-Darcian) to solve
contaminant transport problems. Thus, mathematical solution of
Equations 15-17 by numerical methods does not necessarily lead to an
effective tool for evaluating pesticide leaching risks to groundwater.
Such solutions are valuable in developing fundamental insights into
the governing processes to be sure, but the goal remains to develop
an operational procedure for leaching assessment.
2.3 APPLICATION OF THEORY IN PRZM
Before proceeding in developing the solution to the basic
equations in Section 2.2, it is useful to reconsider the pesticide
leaching problem. Pesticide leaching from field-sized areas is the
major concern. Because most pesticides are applied on, just beneath,
or near the soil surface, the rainfall-infiltration-runoff process
must be described. Movement within and below the root zone is
influenced by soil moisture, which requires continuous soil moisture
accounting in the model. Various field crops are of interest, each
having different growth patterns, rooting depths, and transpiration
requirements. Pesticide transformation process parameters (e.g.,
kinetic rate constants) may vary with soil depth as well as moisture
and other variables so that dispersion and advection have important
interactions with transformation processes. Superimposed on these
13
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factors is the objective to develop efficient, reasonably accurate
solutions obtained from data generally available from national data
bases, maps, and field handbooks.
Equation 16 can be solved numerically if for each time step
the moisture content, 9, and pore velocity, v, are also known.
Furthermore, if "field averaged" values for 0 and v are estimated
then the solution is no longer restricted to only a point value. In
this manner a pseudo-three dimensional solution is obtained with
spatial averages for two dimensions. The accuracy of this approach
is quite sensitive to the distribution function that describes the
field spatial variability. Existence of skewed, non-normal distri-
butions will influence the expected value for leaching and must be
acknowledged and accommodated where possible.
The hydrologic components of Equation 15 (9, v, and Xe) can be
decoupled, solved separately, and used to numerically integrate the
equation in succeeding timesteps. This approach was adopted in the
PRZM model. Three component problems must be solved: (1) water
balance in the soil profile; (2) erosion from the soil surface; and
(3) chemical transport in the soil. Each set of equations will now
be presented as they are solved in the PRZM code.
2.4 WATER BALANCE EQUATIONS
Water balance equations are separately developed for: 1 ) the
surface zone, 2) horizons comprising the active root zones, and 3)
the remaining lower horizons within the unsaturated zone. The
equations are:
Surface Zone
(SW)f+1 = (SW)* + P + SM - I1 - Q - E1 (18)
Root Zone
(SW)f+1 = (SW)J + Ii_1 - Ui - Ii (19)
Below Root Zone
+ l_ - i (20)
where (SW)j^ = soil water in layer "i" of the noted zone on
day "t" (cm)
P = precipitation as rainfall minus crop interception,
cm day"1
SM = snowmelt cm day"1
Q = runoff cm day"1
E- = evaporation, cm day"
14
-------
U- = transpiration cm day"
I. = percolation out of zone i cm day"
Daily updating of soil moisture in the soil profile via the
above equations requires the additional calculations for runoff,
snowmelt, evaporation, transpiration, and percolation. Input
precipitation is read in and pan evaporation and/or air temperature
are inputs providing the potential energy from which evapotranspira-
tion (ET) is estimated.
Incoming precipitation is first partitioned between snow or
rain depending upon temperature. Air temperatures below 0.0°C
produce snow. Precipitation first encounters plant interception and
once the user-supplied storage is depleted the remaining daily volume
is available for the runoff calculation.
The runoff calculation in PRZM is the key element in the water
balance procedure. This calculation partitions the precipitation
between surface runoff and infiltrating water available for leaching.
Runoff is calculated by a modification of the USDA Soil Conservation
Service curve number approach (18). This method was chosen because
it is a reliable procedure used for many years; the required inputs
are generally available; and it relates runoff to soil type, land
use, and management practices.
One modification required for PRZM was the inclusion of snowmelt.
First, snowmelt is estimated on days in which a snow pack exists and
above freezing temperatures occur as
SM = CMT (21)
where CM = degree day snowmelt factor (cm °C day" )
T = average daily temperature (°C)
Precipitation accumulates in the snowpack when the daily average
temperature is below freezing.
The precipitation and or snowmelt are inputs to the SCS runoff
equation written as
(P + SM - 0.2S)2
Q = (22)
P + SM + 0.8S
where S, the watershed retention parameter, is calibrated by
S = 1000/RCN - 10 (23)
15
-------
where RCN = curve number
The curve numbers are a function of soil type, soil drainage
properties, crop type, and management practice. Typically, specific
curve numbers for a given rainfall event are determined by the sum
of the rainfall totals for the previous five days, known as the
five-day antecedent moisture condition. In PRZM, the curve numbers
are uniquely determined each day as a function of the soil water
status in the upper soil layers. These algorithms were developed
and reported by Haith et al. (18).
The daily evapotranspiration demand is divided among evaporation
from canopy, soil evaporation, and crop transpiration. Total demand
is first estimated and then extracted sequentially from crop canopy
storage and from each layer until wilting point is reached in each
layer or until total demand is met. Evaporation occurs down to a
user specified depth. The remaining demand, crop transpiration, is
met from the layers between this depth and the active rooting depth.
The root zone growth function is activated at crop emergence eind
increases step-wise until maximum rooting depth is achieved at crop
maturity.
Actual evapotranspiration demand is estimated as:
i-1
(ET).j_ = MIN [(SW)£ - (WP)j;)*f^, (ET) - £ (ETJJ (24)
1
where (ET)^ = the actual evapotranspiration from layer 'i1 (cm)
fdi = dePtn factor for layer 'i1
(WP)j^ = wilting point water content in layer 'i1 (cm)
(ET)D = potential evapotranspiration (cm)
The depth factor, f^i/ is internally set in the code. It lin«;arly
weights the extraction of ET from the root zone with depth in a
triangular fashion. That is, a triangular root distribution is
assumed from the surface zone to the maximum depth of rooting with
the maximum root density assumed to be near the surface.
ET also is limited by soil moisture availability. The potential
rate may not be met if sufficient soil water is not available to
meet the demand. PRZM modifies the potential by the following
equations
ETp = ETp if SW _>. 0.6 FC (25)
ET = SMFAC*ET if WP < SW < 0.6 FC
ETp = 0 if SW <_ WP
16
-------
where FC = soil moisture content at field capacity
WP = soil moisture content at wilting point
SMFAC = soil moisture factor
The SMFAC parameter has been investigated in other similar water
balance models (18, 49) and is internally set in the code to linearly
reduce ETp according to the limits imposed in Equations 24 and 25.
Finally, if pan evaporation input data are available, ETp is related
to the input values as
ETp = Cp * PE (26)
where PE = pan evaporation (cm day~1)
C = pan factor, dimensionless
The pan factor is constant for a given location and is a function of
the average daily relative humidity, average daily windspeed, and
location of the pan with respect to an actively transpiring crop.
In the absence of pan evaporation data, ETp is estimated by
ET = 14000 L| (SVD) (27)
where Ly = possible hours of sunshine per day, in 1 2-hour units
SVD = saturated vapor density at the mean air temperature,
(g cm"1)
SVD = 0.622(SVP)/(Rg * Tabs)
where (SVP) = saturated vapor pressure at the mean absolute air
temperature, (mb)
Rq = dry-air gas constant
Tabs = absolute mean air temperature (°K)
The final term in the water balance equations that must be
defined is the percolation value, I. The use of the SCS curve number
approach for runoff precludes the direct use of a Darcian model.
PRZM resorts to "drainage rules" keyed to soil moisture storages and
the time available for drainage. Two options are included. Both
are admittedly simplistic representations of soil moisture redistri-
bution, but are consistent with the intent of model and its future
uses .
Option 1.
The percolation, I, in this option is defined in the context of
two bulk soil moisture holding characteristics commonly reported for
agricultural soils: field capacity and wilting point. Field capacity
is a somewhat imprecise measure of soil water holding properties and
17
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is usually reported as the moisture content that field soils attain
after all excess water is drained from the system under influence of
gravity. The difficulty with this concept is the fact that some
soils will continue to drain for long periods of time and thus field
capacity is not a constant. Given the lack of theoretical and
physical rigor, the concept remains as a useful measure of soil
moisture capacity and has been successfully used in a number of
water balance models (18, 49). Wilting point of soils is a function
of both soils and plants. It is defined as the soil moisture content
below which plants are unable to extract water.
Field capacity and wilting point are used operationally to
define two reference states in each soil layer for predicting
percolation. If the soil water, SW, is calculated to be in excess of
field capacity, then percolation is allowed to remove the excess
water to a lower zone. The entire soil profile excess is assumed to
drain within one day. The lower limit of soil water permitted is
the wilting point. One outcome of these assumed "drainage rules" is
that the soil layers below the root zone quickly reach field capacity
and remain at that value. When this condition is reached, alL water
percolated below the root zone will displace the water within the
lower soil layer simulated and so on. There is no allowance for
lateral water movement. Water balance accounting in this manner
should be most accurate for sandy soils and is least accurate for
tight, clay soils (49). Fortunately, the greatest concern for
leaching arises for sandy, loose soils.
Option 2 .
The second option is provided to accommodate soils having low
permeability layers that restrict the "free drainage" assumed in
Option 1 . In the context of the field capacity reference condition,
two things may occur. First, conditions may prevail that raise the
soil moisture levels above field capacity for periods of time because
the water is "backed up" above a relatively impermeable layer.
Second, the excess water may not drain during the one-day period
assumed in Option 1 . To accommodate these conditions two additional
parameters are needed. Maximum soil moisture storage, 9*, is added
to represent moisture contents under saturated conditions. The
drainage rate also must be modified to allow drainage to field
capacity over periods in excess of one day (model one time-step) .
This is accomplished by adjusting the end of time step moisture content
by
exp(- oAt) + 9fci (28)
where 9 = soil layer water content (cm-^ cm~3)
9f = water content at field capacity (cm cm~ )
18
-------
a = drainage rate parameter (day 1)
In this equation t and t+1 denote beginning and end of time-step
values, respectively, and i is the soil layer index. The value t*
denotes a value of time between beginning and end of time-step. The
variable 0.: here denotes current storage plus any percolation from
the next layer above, before the occurrence of any drainage from the
current layer. Because Equation 28 is solved independently for
each layer in the profile, there is a possibility of exceeding the
storage capability (saturation water content, Qs) of a low permeabi-
lity layer in the profile if a more permeable layer overlies it. At
each timestep, once redistribution is complete, the model searches
the profile for any 9i>9si. If this condition is found, the model
redistributes water back into overlying layers, as if the percolation
of additional water beyond that necessary to saturate the low permea-
bility layer had not occurred. This adjustment is necessary due to
the nature of Equation 28 and the fact that these equations for each
layer are not easily coupled. The difficulty in coupling the equations
for the entire profile arises from the dicotomy that one of two factors
limits percolation from a stratum in the profile; either the rate at
which that stratum can transmit water, or the ability of the stratum
below it to store or transmit water. This dicomtomy leads to an
iterative (or at least corrective) approach to the explicit solution
of a system of equations for 9i, represented by Equation 28. It
should be noted, however, that the value of a selected by this approach
is only relevant if the permeability of the soil materials, and not
sortage considerations in the profile (i.e., the presence of a water
table), is the limiting factor for percolation of water.
2.5 EROSION EQUATIONS
Removal of sorbed pesticides on eroded sediments requires
estimates for soil erosion. PRZM operates on a daily time-step and
hence only daily storm event totals are estimated suggesting at most
a total storm event resolution for the erosion calculation. The
Modified Universal Soil Loss Equation (MUSLE) as developed by Williams
(56) was selected. Soil loss is calculated by
xe = a (Vrqp)°*55KLSCP (29)
where V = volume of event (daily) runoff (m )
3—1
q = peak storm runoff (m sec )
K = soil erodability factor
LS = length-slope factor
C = soil cover factor
P = conservation practice factor
a = units conversion factor
Most of the parameters in Equation 29 are easily determined from
other calculations within PRZM (e.g., Vr) and others are familiar
19
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terms readily available from handbooks. The peak storm runoff, q ,
is not so easily characterized. In general, values for q vary
widely and respond to precipitation and runoff dynamics. The daily
total rainfall-runoff procedures adopted for PRZM do not allow
individual storm event resolution of the hydrograph. Rather, a
trapezoidal hydrograph is assumed with a user-specified average
storm duration. From the assumed hydrograph shape and the storm
duration, a peak runoff is calculated once the volume is estimated
from Equation 22.
The enrichment ratio, rom, is the remaining term in the overall
transport equation (Equation 15) to be defined. Recall that because
erosion is a selective process during runoff events, eroded sediments
become "enriched" in smaller particles. The sediment transport the-
ory available to describe this process requires substantially more
hydraulic spatial and temporal resolution than used in PRZM leading
us to adopt an empirical approach (33). The enrichment ratio for
organic matter is calculated from
ln(rom) =2+0.2 ln(Xe/Aw) (30)
2.6 CHEMICAL TRANSPORT EQUATIONS
The second-order partial differential equation outlined in
Section 2.2 must be solved with appropriate boundary conditions. A
decoupled approach is taken. That is, the calculations for moisture
contents, pore velocities, erosion, and runoff are decoupled from
Equation 15 and solved separately. The resulting values, treated as
constants for each specific time step, are then used as coefficients
in a finite difference approximation of the chemical transport equa-
tion. A backwards difference, implicit scheme is used with a spatial
and time step equal to those used in the water balance equations.
The resulting difference equations are solved for a new dissolved
pesticide concentration, Cw, at the end of the timestep.
For boundary conditions the numerical scheme uses
Cw ei-l = ° for i = 1 (31 )
W
and
C-w. ei+1 ~ cwi ei
= o for i = N (31a)
Ax
where N = total number of compartments .
These conditions correspond to a zero concentration at the soil
surface and a concentration gradient of zero at the bottom surface
of the soil profile.
20
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SECTION 3
MODEL STRUCTURE AND DATA INPUT
3.1 INTRODUCTION
A detailed flow diagram of the PRZM structure is provided in
Figure 3. PRZM consists of blocks of FORTRAN statements by which
computational tasks are performed. The descriptions in Figure 3
appear as comment statements (descriptive headings) to the blocks of
FORTRAN statements. Should the user choose to edit the code, the
anticipated change can be easily located within the program by the
comment statements. This section provides an overview of the PRZM
structure and parameter file.
3.2 PRZM STRUCTURE
A listing of program variables, units, and definitions are found
in the programmer's guide (Appendix E). The dimensioning requirements,
common blocks, and program structure also are described.
The major functions currently performed by PRZM are:
• data input
• calculation of soil moisture characteristics based on
textural properties
• calculations of K^ based on water solubility models
• echo of inputs to output files
• determination of crop root growth
• meteorological time series data input
• crop interception of rainfall
• division of precipitation between rain and snow
• calculation of evapotranspiration
• snowmelt computation
21
-------
Open I/O files
Read input parameters
Initialize program variables |
X
Begin annual program loop
Begin daily program loop
Calculate crop parameters
Perform hydrology calculations
Perform soil hydraulic computations
esticide
application
date
Apply
pesticide
Plant pesticide calculations
Soil pesticide calculations
Return
day
loop
Return
yearly
loop
Perform mass balance
Perform indicated output routines
nd date
(day)
reached
Last year
reached
End of year
output
Figure 3. Generalized flow chart of Pesticide Root Zone Model,
22
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calculation of plant uptake factors
determination of curve number from cropping period
and soil moisture
computation of runoff and infiltration
calculation of soil hydraulics
calculation of pesticide transport in soil
pesticide application
water and pesticide mass balance computation
output of fluxes, storages, etc.
input checking
foliar pesticide application decay and washoff
soil erosion and erosion pesticide loss
PRZM is a module-oriented model and contains several subroutines
that calculate the functions provided in Section 3.2. A listing of
subroutine names and corresponding functions are provided below.
SUBROUTINE NAMES FUNCTIONS
ECHO Echoes inputs from READ to an output file,
checks data and prints warning messages
for improper input values.
EROSN Calculates erosion sediment loss and
enrichment ratio for chemical.
EVPOTR Computes potential evapotranspiration, soil
evaporation and transpiration.
FTIME Provides current time and date of simula-
tion run.
HYDROL Calculates crop interception, snow melt,
runoff and surface water infiltration.
INITL Initializes all program storages.
KDCALC Calculates K^ by one of three models if
invoked.
23
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MAIN PROGRAM
MASBAL
OUTCNC
OUTHYD
OUTPST
OUTTSR
PESTAP
PLGROW
PLPEST
READ
SLPEST
SOILHY
1)HYDR1
2)HYDR2
THCALC
TRDIAG
Provides management of above subroutines,
sets up program simulation loops, reads
and checks time varying input data, determines
types of outputs and output schedules.
Performs water and pesticide mass balances.
Outputs pesticide concentration profiles.
Accumulates fluxes and outputs summary
information for water.
Accumulates fluxes and outputs summary
information for pesticides.
Outputs specific time series to plotting
files.
Distributes pesticide appliation to
either plant foliage, soil surface,
or soil incorporation depths.
Calculates pertinent crop growth parameters.
Performs plant pesticide decay and
washoff calculations.
Reads time variant inputs and those that
vary on a greater-than-daily time-step.
Performs units conversions.
Calculation terms for pesticide decay,
movement, adsorption, runoff, erosion,
etc., in soil.
Performs soil hydraulic computations.
Well drained soils.
Poorly drained soils.
Calculates field capacity, wilting point
and saturation potentials from soil
textural information if invoked.
Solves for new vector of soil pesticide
concentrations.
Within the main program are the open statements. These files are
necessary to process (run) the job submission. There are six files in
PRZM—two data input files and four result output files.
24
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OPEN UNIT = 2 or 4 is the meteorological file
OPEN UNIT = 3 or 5 is the model parameter file
OPEN UNIT = 4 or 6 is the hydrologic result file
OPEN UNIT = 7 is the chemical result file
OPEN UNIT = 8 is the pesticide concentration output file
OPEN UNIT = 9 is the time series output file
3.3 DATA INPUT
The remainder of this section will describe the development of
data input files. A brief description of the parameter is followed
by a more detailed discussion (Section 4) that will aid the user in
assigning values to specific input parameters. Data inputs to para-
meter file can be easily created by using the ANPRZM pre-processing
module, as described in Section 5.
3.3.1 Meteorological File (UNIT = 2 or 4)
Information for one day only is included in each line (card) of
the meteorological file.
READ(4,100,END=999) MM, MD, MY, PRECIP, PEVP, TEMP
100 FORMAT (1x, 312, 3F10.0) [Model Code]
(1234567890123456789012345678901234567890123456789) [COLUMN NUMBER]
010179 1.50 0.340 17.2 [Example Card]
The format identifier, 312, indicates that there are six spaces (col-
umns) for designating the month (MM), day (MD), and year (MY) of the
meteorological data. The example 010179 indicates month 01, day 01,
and year 1979. The 3F10.0 indicates that PRECIP, PEVP, and TEMP are
to be found in three separate blocks consisting of 10 columns each.
PEVP and TEMP are not always required together; various combinations
are possible depending on the observed data or climate (i.e. geographi-
cal areas having major snow accumulation will require temperature data).
3.3.2 Parameter File (UNIT = 3 or 5)
Each line (representing a card) in the parameter file has a speci-
fied number of parameters in it. Each line has a formatted designation
and is right justified. The user should make sure that the parameters
for each line (card) required for a specific run has a value specified
25
-------
so that the READ statement will not go to the next line searching
for a parameter file value (that would initiate an error message).
Each line and the input data parameters for each line are discussed
below (in the order required by the model).
CARD 1. TITLE
FORMAT (20A4)
TITLE(10): A specific title is developed for the simulation and
it appears in all three result files, e.g., Calibration Run Albany,
Georgia. A total of 80 characters can be input to the title card.
CARD 2. ISDAY, ISMON, ISTYR, IEMON, IEDAY, IEYR
FORMAT (2X, 312, 10X, 212)
ISDAY: Starting day of simulation, e.g., 1 =February 1st.
ISMON: Number of the starting month of simulation, e.g.,
2 =February.
ISTYR: Starting year of simulation, e.g., 79.
IEDAY: Ending day of simulation, e.g., 31 =December 31,
IEMON: Number of the ending month of simulation, e.g.,
12 =December.
IEYR: Ending year of simulation, e.g., 80.
CARD 3. HTITLE
FORMAT (20A4)
HTITLE(10): This card provides a comment line of 80 characters
for the user to input information regarding hydrology parameters.
CARD 4. PFAC, SFAC, IPEIND, ANETD, INICRP, ISCOND
FORMAT (2F8.0, 18, F8.0, 218)
PFAC: Pan factor, dimensionless. This factor is multiplied
by daily pan evaporation to estimate daily evapotrans-
piration (ET). If daily air temperatures are used
for ET, any dummy number can be input for PFAC (e.g.,
0.75)
SFAC: Snow factor, cm snowmelt/°C above freezing. Values of
snow factor are in the order of 0.45. If snowmelt is
not calculated, enter 0.00 for SFAC.
26
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IPEIND: Pan evaporation flag. If IPEIND = 0, pan evaporation
data are read. If IPEIND = 1, temperature data are
read and used to calculate potential ET. If IPEIND =
2, then pan evaporation, if available, is used in the
meteorologic file; if not, temperature is used to
compute potential ET.
ANETD: Minimum depth, cm, in which evaporation is extracted
yearly (e.g., 20.0).
INICRP: User specified initial crop number if simulation date
is before first crop emergence date (see card 9).
ISCOND: User specified surface condition after harvest corre-
sponding to INICRP (either fallow cropping, or residue,
corresponding to dimensionless integer of 1 , 2 or 3).
CARD 4A. DT (Only if IPIEND = 1 or 2; DO NOT include this card if
IPIEND = 0)
FORMAT (8F8.0)
DT(12): Average daily hours of daylight for each month. A
total of 12 values (one for each month) that are
input using two lines in the parameter file.
CARD 5. ERFLAG
FORMAT (18)
ERFLAG:
Erosion flag. If erosion losses are not to be calcu-
lated, ERFLAG = 0, otherwise ERFLAG = 1 .
CARD 5A. USLEK, USLELS, USLEP, AFIELD, TR (Only if ERFLAG = 1 ;
DO NOT include this card if ERFLAG = (3).
FORMAT (5F8.0)
USLEK: Universal soil loss equation (K) soil erodibility para-
meter (e.g., 0.15).
USLELS: Universal soil loss equation (LS) topographic factor
(e.g., 0.14).
USLEP: Universal soil loss equation (P) supporting practice
factor (e ,g., 1.0).
AFIELD: Area of field or plot (ha).
TR: Average duration of runoff hydrograph from runoff pro-
ducing storms (hrs.).
27
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CARD 6. NDC
FORMAT (18)
NDC:
Number of different crops used in the simulation
(minimum of 1).
CARD 7. ICNCN, CINTCP, AMXDR, COVMAX, ICNAH, CN, USLEC, WFMAX
FORMAT (18, 3F8.0,I8, 3(1X, 13), 3(1X, F3.0), F8.0)
NOTE: One card each must be read in to match the total
number of crops (NDC).
ICNCN: Crop number.
CINTCP: Maximum interception storage of the crop (cm).
AMXDR: Maximum active root depth of the crop (cm).
COVMAX: Maximum areal coverage of the crop at full canopy
(percent).
ICNAH: Soil surface condition after crop harvest (1 = fallow,
2 = cropping, 3 = residue).
CN: Runoff curve number for the antecedent soil water con-
dition II, for fallow, crop, and residue fractions of
the growing season (e.g. 86, 78, 82).
USLEC: Universal soil loss equation cover management factor.
Three values must be entered in the same order as (CN),
fallow, crop, and residue. Values only are required if
ERFLAG = 1. Leaving them in the input stream will have
no effect if ERFLAG = J3 (e.g., 0.20)
WFMAX: Maximum dry foliage weight of the crop at full canopy
kg m~2. Only required if the exponential filtration
model is used for pesticide application (values of
WFMAX will not affect the simulation if FAM = 1 or 2,
see card 13).
CARD 8. NCPDS
FORMAT (18)
NCPDS:
Number of cropping periods in the simulation (minimum
of 1) . If three cropping years of continuous corn
are simulated, NCPDS =3. If two winter cover crops
are in the middle of the three years of corn, NCPDS =
5.
28
-------
CARD 9. EMD, EMM IYREM, MAD, MAM, IYRMAT, HAD, HAM, IYRHAR, INCROP
FORMAT (2X, 312, 2X, 312, 2X, 312, 18)
NOTE: One card each must be read in to match the total
number of cropping periods (NCPDS).
EMD: Day of month of crop emergence (e.g., 20).
EMM: Month of crop emergence (e.g., 4).
IYREM: Year of crop emergence (e.g., 82).
MAD: Day of month of crop maturation (e.g., 15).
MAM: Month of crop maturation (e.g., 10).
IYRMAT: Year of crop maturation (e.g., 82).
HAD: Day of month of crop harvest (e.g., 20).
HAM: Month of crop harvest (e.g., 10).
IYRHAR: Year of crop harvest (e.g., 82).
INCROP: Crop number of crop growing in current period (e.g., 1),
CARD 10. PTITLE
FORMAT (20A4)
PTITLE(10): This card provides a comment line of 80 characters
for the user to input information regarding pesticide parameters.
CARD 11. NAPS
FORMAT (18)
NAPS: Number of pesticide applications (minimum of 1) .
CARD 12. APD, APM, IAPYR, TAPP, DEPI
FORMAT (2X, 312, 2F8.0)
NOTE: One card should be entered for each application up to
the number of applications (NAPS).
APD: Day of the month of pesticide application (e.g., 10).
APM: Month of pesticide application (e.g., 5).
29
-------
IAPYR: Year of pesticide application (e.g., 82).
TAPP: Total pesticide application (kg ha~1) .
DEPI: Depth of pesticide incorporation (cm).
CARD 13. FAM
FORMAT (18)
FAM: Pesticide application model. There are three options:
FAM = 1 indicates application to soil only, FAM = 2
indicates a foliar application using the linear
model, and FAM = 3 indicates a foliar application
using the exponential filtration model.
CARD 13A. PLDKRT, FEXTRC, FILTRA (Only if FAM = 2 or 3; DO NOT
include this card if FAM = 1 ).
FORMAT (3F8.0)
PLDKRT: Pesticide decay rate on plant foliage (days"1)
FEXTRC: Foliar extraction coefficient for pesticide washoff
per centimeter of precipitation (e.g., 0.10).
FILTRA: Filtration parameter for exponential model (only
required if FAM = 3).
CARD 14. STITLE
FORMAT (20A4)
STITLE(10): This card provides a comment line of 80 characters
for the user to input information regarding soils properties.
CARD 15. CORED, UPTKF, NCOM2, BDFLAG, THFLAG, KDFLAG, HSWZT
FORMAT (2F8.0, 518)
CORED: Total depth of soil core (cm).
UPTKF: Plant uptake efficiency factor; UPTKF = (3 indicates
no plant uptake simulated, UPTKF = 1 indicates uptake
is simulated and is equal to the crop transpiration
rate, 0
-------
BDFLAG: Bulk density flag; BDFLAG = J3 indicates apparent bulk
density known and entered (see CARD 17), BDFLAG = 1
indicates apparent bulk density to be calculated and
mineral bulk density entered (see CARD 17).
THFLAG: Calculation flag for soil field capacity and wilting
point water contents; THFLAG = (3 indicates water
contents known and entered (see CARD 17A), THFLAG = 1
indicates water contents are not known and will be
calculated.
KDFLAG: Calculation flag for soil/pesticide sorption partition
coefficients; KDFLAG = j3 indicates partition coeffi-
cients known and entered (see 17A), BDFLAG = 1 indicates
partition coefficients not known and will be calculated.
HSWZT: Switch for soil hydraulics; HSWZT = J3 indicates free
draining soils, HSWZT = 1 indicates restricted draining
soils.
CARD 15A. PCMC, SOL (Only if KDFLAG = 1, DO NOT include if KDFLAG = J3)
FORMAT (18, F8.0)
PCMC: Calculation flag for model to estimate pesticide soil
partition coefficients. There are three options:
PCMC = 1, PCMC = 2, and PCMC = 3.
SOL: Pesticide solubility. The units vary according to
the model (PCMC) selected; PCMC = 1, mole fraction;
PCMC = 2, mg liter"1; PCMC = 3, micromoles liter"1.
CARD 16. NHORIZ
FORMAT (18)
NHORIZ: Total number of soil horizons (minimum of 1 ) .
CARD 17. HORIZN, THKNS, BD, DISP, DKRATE, THETO, AD
FORMAT (18, 6F8.0)
HORIZN: Soil horizon number.
THKNS: Soil horizon thickness (cm).
BD: Soil bulk density (if BDFLAG = (2) and/or mineral bulk
density (if BDFLAG = 1 ).
DISP: Hydrodynamic dispersion (cm2 day"1).
31
-------
DKRATE: Pesticide decay rate in the soil horizon (days~1) .
THETO: Initial soil water content in the horizon (cm3 cm"3).
AD: Soil horizon drainage parameter (1 day"^), used only
if HSWZT = 1, otherwise, the value is ignored.
NOTE: Cards 17A, 17B, 1 7C and/or 17D are read in (as a
continuation of CARD 17) for each soil horizon up
to number of horizons (NHORIZ) input (CARD 16).
CARD 17A. THEFC, THEWP, KD, OC (Only if THFLAG = 0 and KDFLAG = 0)
FORMAT (8X, 4F8.0)
THEFC: Field capacity soil water content of horizon (cm3 cm"3).
THEWP: Wilting point soil water content of horizon (cm3 cm"3).
KD: Sorption partition coefficient for soil horizon/pesti-
cide combination (cm3 g~1).
OC: Organic carbon content of soil horizon (percent). This
value is also required if BDFLAG = 1.
CARD 17B. THEFC, THEWP, OC (Only if THFLAG = 0 and KDFLAG = 1)
FORMAT (8X, 3F8.0)
THEFC: Field capacity soil water content of horizon (cm3 cm"3).
THEWP: Wilting point soil water content of horizon (cm3 cm~3).
OC: Organic carbon content of soil horizon (percent). This
value is also required if BDFLAG = 1.
CARD 17C. SAND, CLAY, OC, KD (Only if THFLAG = 1 and KDFLAG = J2»
FORMAT (3X, 4F8.0)
SAND: Percent sand in soil horizon.
CLAY: Percent clay in soil horizon.
OC: Organic carbon content of soil horizon (percent). This
value is also required if BDFLAG = 1.
KD: Sorption partition coefficient for soil horizon/pesti-
cide combination (cm3 g~1 ) .
32
-------
CARD 17D. SAND, CLAY, OC (Only if THFLAG = 1 and KDFLAG = 1)
FORMAT (8X, 3F8.0)
SAND: Percent sand in soil horizon.
CLAY: Percent clay in soil horizon.
OC: Organic carbon content of soil horizon (percent). This
value is also required if BDFLAG = 1.
CARD 18. ILP, CFLAG
FORMAT (218)
ILP: Initial level of pesticide indicator. Signals user
to input an initial pesticide storage. ILP = (3,
indicates no initial levels input; ILP = 1, indicates
initial levels are being input.
CFLAG: Conversion flag for initial pesticide level input.
CFLAG = 0, indicates input in mg kg"1; CFLAG = 1,
indicates input in kg ha~1. This flag need not be
assigned if ILP = 0.
CARD 18A. PESTR (Only if ILP = 1)
FORMAT (8F8.0)
PESTR: Initial pesticide level in each compartment (up to
NCOM2) as entered from CARD 15. Input must be either
in mg kg"1 or kg ha~1.
CARD 19. ITEM1, STEP1, LFREQ1, ITEM2, STEP2, LFREQ2, ITEM3, STEP3,
LFREQ3
FORMAT (3 (4X, A4, 4X, A4, 18)
NOTE: For hard copy output.
ITEM1: Hydrologic output summary indicator. WATR is in-
serted to call hydrologic summaries. A blank is left
for ITEMl if hydrologic summaries are not desired.
STEPl: Time step of output. Three options are available:
DAY for daily, MNTH for monthly, or YEAR for annual
output.
LFREQ1: Frequency of soil compartment reporting. Example:
LFREQ1 = 1, every compartment is output; LFREQ = 5,
every fifth compartment is output.
33
-------
ITEM2: Pesticide output summary indicator. PEST is inserted
to call pesticide summaries (of mass migration). A
blank is inserted for ITEM2 if pesticide summaries
are not desired.
STEP2: Same as STEPl.
LFREQ2: Same as LFREQ1.
ITEM3: Pesticide concentration profile indicator. CONG is
inserted to call pesticide concentration profile
summaries. A blank is inserted if concentration
profiles are not desired.
STEP3: Same as STEPl.
LFREQ3: Same as LFREQ1.
CARD 20. NPLOTS
FORMAT (18)
NOTE: Cards 20 and 21 are for internal times series output
files for later use.
NPLOTS: Number of time series to be written to plotting file
(maximum of 7).
CARD 21. PLNAME, MODE, IARG, CONST (Only if NPLOTS is greater than
zero)
FORMAT (4X, A4, 4X, A4, 18, F8.0)
PLNAME: Identifier of time series. Possible options are listed
in Table 1.
MODE: Plotting mode. Two options are available: TSER pro-
vides the time series as output, TCUM provides the
cumulative time series.
IARG: Argument of variable identified in PLNAME. Example:
INFL is specified which corresponds to AINF within
the FORTRAN program. AINF is dimensioned from 1 to
NCOM2. IARG must be specified to identify the soil
compartment (1 to NCOM2) reporting for AINF (IARG
is left blank for sealers).
CONST: Specifies a constant with which the user can multiply
the times series for unit conversion, etc. If left
blank a default of 1.0 is used.
34
-------
Table 1. Variable Designations for Plotting Files
Variable
Designation
(PLNAME)
FORTRAN
Variable
Description
Units
Arguments
Required
(IARG)
Water Storages
INTS CINT
SWTR SW
SNOP SNOW
THET THETN
Water Fluxes
PROP PRECIP
SNOF SNOWFL
THRF THRUFL
INFL AINF
RUNF RUNOF
CEVP CEVAP
SLET ET
TETD TDET
Sediment Flux
ESLS SEDL
Pesticide Storages
EPST FOLPST
TPST PESTR
Interception storage
on canopy
Soil water storage
Snow pack storage
Soil water content
Precipitation
Snowfall
Canopy throughfall
Percolation into each
soil compartment
Runoff depth
Canopy evaporation
Actual evapotrans-
piration from each
compartment
Total daily actual
evapotranspiration
Event soil loss
Foliar pesticide
storage
Total soil pesticide
storage in each soil
compartment
cm
cm
cm
None
1-NCOM2
None
cm cm"1 1-NCOM2
cm day"1 None
cm day"1 None
cm day"1 None
cm day"1 1-NCOM2
cm day"1 None
cm day"1 None
cm day"1 1-NCOM2
cm day"1 None
Tonnes
day"1
None
g cm
-2
g cm
-3
None
1-NCOM2
35
-------
Table 1. Variable Designations for Plotting Files (Continued)
Variable
Designation
(PLNAME)
Pesticides Storages
SPST
Pesticide Fluxes
TPAP
FPDL
WFLX
DFLX
AFLX
DKFX
UFLX
RFLX
EFLX
RZFX
FORTRAN
Variable
SPESTR
TAPP
FPDLOS
WOFLUX
DFFLUX
ADFLUX
DKFLUX
UPFLUX
ROFLUX
ERFLUX
RZFLUX
Description
dissolved pesticide
storage in each soil
compartment
Total pesticide
application
Foliar pesticide
decay loss
Foliar pesticide
washoff flux
Individual soil
compartment pesticide
net diffusive flux
Pesticide advective
flux from each soil
compartment
Pesticide decay flux
in each soil compartment
Pesticide uptake flux
from each soil com-
partment
Pesticide runoff flux
Pesticide erosion flux
Net pesticide flux
past the maximum root
Units
g cm" 3
g cm" 2
day-1
g cm~2
day-1
g cm"2
day-1
g cm~2
day"1
g cm~2
day~1
g cm" 2
day~1
g cm"2
day"1
g cm" 2
day"1
g cm~2
day"
g cm"2
day-1
Arguments
Required
( IARG )
1 -NCOM2
None
None
None
1 -NCOM2
1 -NCOM2
1 -NCOM2
1 -NCOM2
None
None
None
TUPX
TDKF
depth
SUPFLX Total pesticide uptake
flux from entire soil
profile
SDKFLX Total pesticide decay
flux from entire profile
g cm"2 None
day~1
g cm"2 None
day~1
36
-------
Table 1A. Conversion Factors for English and Metric Units3
To Convert
Column 1
into Column 2,
Multiply by
Length
0.621
1 .094
0.394
Area
0.386
247.1
2.471
Vo lume
0.00973
3.532
2.838
0.0284
1 .057
Mass
1 .102
2.205
2.205
0.035
Pressure
14.50
0.9869
0.9678
14.22
14.70
Yield or Rate
0.446
0.892
0.892
Column 1
kilometer, km
meter, m
centimeter, cm
kilometer2, km2
kilometer2, km2
hectare, ha
meter^, m^
hectoliter, hi
hectoliter, hi
liter
liter
tonne (metric)
quintal, q
kilogram, kg
gram, g
bar
bar
kg (weight) /cm2
kg (weight) /cm2
atmosphere,'3 atm
ton (metric) /hectare
kg /ha
quintal/hectare
Column 2
mile, mi
yard, yd
inch, in
mile2, mi2
acre, acre
acre, acre
acre-inch
cubic foot, ft3
bushel, bu
bushel, bu
quart (liquid), qt
ton (English)
hundredweight,
cwt (short)
pound, Ib
ounce (avdp), oz
lb/inch2, psi
atmosphere,'3 atm
atmosphere,0 atm
Ib/inch2, psi
lb/inch2, psi
ton (English) /acre
Ib/acre
hundredweight/acre
To Convert
Column 2
into Column
1 , Multiply by
1 .609
0.914
2.54
2.590
0.00405
0.405
102.8
0.2832
0.352
35.24
0.946
0.9072
0.454
0.454
28.35
0.06895
1 .01 3
1 .033
0.07031
0.06805
2.240
1 .12
1 .12
37
-------
Table 1A. Conversion Factors for English and Metric Unitsa
(Continued)
To Convert
Column 1
into Column 2,
Multiply by Column 1
Column 2
To Convert
Column 2
into Column
1, Multiply by
Temperature
(2 °C) + 32
5
Celsius
-17.8C
OC
20C
100C
Fahrenheit
OF
32F
68F
212F
- 32)
Water Measurement
8.108
97.29
0.08108
0.9729
0.00973
0.981
440.3
0.00981
4.403
hectare-meters, ha-m
hectare-meters, ha-m
hectare-centimeters, ha-cm
hectare-centimeters, ha-cm
meters^, m^
hectare-centimeters/
hour, ha-cm/hour
hectare-centimeters/
hour, ha-cm/hour
meters^/hour, m^/hour
meters-^/hour , m^/hour
acre-feet
acre-inches
acre-feet
acre-inches
acre-inches
f eet-3/sec
U.S. gallons/min
feet^/sec
U.S. gallons/min
0.1233
0.01028
12.33
1 .028
102.8
1 .0194
0.00227
101 .94
0.227
aSoil Sci. Soc. of Amer. J., Vol. 44, No. 4, 1980.
size of an "atmosphere" may be specified in either metric or
English units
38
-------
SECTION 4
PARAMETER ESTIMATION
4.1 INTRODUCTION
PRZM relates pesticide leaching to temporal variations of hydrology,
agronomy, and pesticide chemistry. A minimum of generally accessible
input is required for successful use of PRZM. The model does utilize
some parameters, however, that users may find difficult to obtain or
calculate. The following section describes these parameters and provides
detailed procedures for estimating or obtaining the required values. The
following section is structured in the same general order that the para-
meters appear in the parameter file. Options are available in the pro-
gram to directly estimate several parameters (THEFC, THEWP, BD, and
KD) when related information is supplied by the user.
4.2 HYDROLOGY
4.2.1 Snow Factor and Pan Factor—(SFAC and PFAC) Card 4
When the mean air temperature falls below 0.0 °C, any precipitation
that falls is considered to be in the form of snow. When the mean air
temperature is above 0.0 °C, however, the snow accumulation is decreased
by a snowmelt factor, SFAC. The amount of snowmelt is calculated by the
degree-day factor and was described in Section 2 (Theory).
The mean air temperature is read from the meteorological file and
provides a value for (T). The snowmelt factor, SFAC, for site specific
analyses can be obtained from Linsley, Kohler, and Paulhus (31). The
mid-range of their values is 0.457 cm day~^°C~^« The calculated snow
melt is used to estimate the antecedent moisture condition and subse-
quently the runoff caused by the snowmelt. The snow factor would be
applicable only to those areas where the climatology lends to tempera-
tures conducive to snow fall and snow melt.
The pan factor (PFAC) is a dimensionless number used to convert
daily pan evaporation to daily potential ET. The pan factor generally
ranges between (0.60-0.80). Figure 4 illustrates typical pan factors
in specific regions of the United States.
39
-------
cd
QJ
PQ
0)
.c
4-)
cd
QJ
13
o
J-l
en
M
O
4-1
O
a
o
•H
4-1
O
0)
M
S-i
O
o
a
o
•H
4J
Ifl
J-l
O
55
s
0)
M
60
•M
40
-------
4.2.2 Soil Evaporation Moisture Loss During Fallow, Dormant Periods—
(ANETD) Card 4
The soil water balance model considers both soil evaporation and
plant transpiration losses and updates the depth of root extraction.
The total ET demand is subtracted sequentially in a linearly weighted
manner from each layer until a minimum moisture level (wilting point) is
reached within each layer. Evaporation is initially assumed to occur in
the top 10 cm of the soil profile with the remaining demand, crop trans-
piration, occurring from compartments below the 10-cm zone and down to
the maximum depth of rooting. These assumptions allow simulation of
reduced levels of ET during fallow, dormant periods and increased levels
during active plant growth. Values for (ANETD) used to estimate soil
evaporation losses are provided in Figure 5.
The values for ANETD in Figure 5 are only applicable for hydrology
option 1, the free drainage model, and would not be appropriate for use
with hydrology option 2, the limited drainage model. The limited drain-
age model allows more available soil water and, hence, more ET extrac-
tion. If drainage option 2 is selected, it is recommended that ANETD be
initially set to equal 10 cm. Further calibration may be required if
results are not consistent with local water balance data.
4.2.3 Average Day Time Hours for a Day in Each Month—(DT) Card 4A
The values of DT are used to calculate total potential ET using
Hamon's Formula if daily pan evaporation data do not exist. Values of
DT for latitudes 24 - 50° north of the equator are provided in Table 2.
Values for DT are determined by:
Step 1. Finding the approximate degree latitude north of the
equator for the agricultural use site under
consideration.
Step 2. Inputting the twelve monthly numbers under the
degree latitude column into the parameter file( e.g.,
42° north latitude).
9.4, 10.4, 11.7, 13.1, 14.3, 14.9, 14.6, 14.0,
12.3, 10.9, 9.7, 9.0
4.2.4 Soil Erosion - Universal Soil Loss Equation—(TR, USLEK, USLELS, USLEP
USLEC,) Cards 5A and 7
The role of erosion and pesticide loss on sediments decreases
with decreasing chemical affinity for soil. The total mass of pesticide
loss for most highly soluble pesticides will be quite small. For such
situations, erosion losses can be neglible. To accommodate these condi-
tions, the erosion flag (see Section 3) can be set equal to 0 (erosion
losses not estimated) . If the apparent distribution coefficient is less
41
-------
E E
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cd
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60
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&0
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Pu
I
42
-------
0
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(/)
.latitude
^4 tO
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4H O
cn TJ
UH
0) 0
s
4J 4-1
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rH 0
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ro
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Dec. 1
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43
-------
than or equal to 5.0, erosion can be neglected. For a compound having a
distribution coefficient greater than 5.0, erosion losses (and subsequent
pesticide loss) should be estimated and the erosion flag set accordingly.
Soil characteristics, climatic conditions, agronomic practices, and
topography contribute to the potential erosion rate from a field. During
an erosion-producing runoff event, soil particles and aggregates are
carried across the field. These aggregates consist of coarse, medium,
and fine particles, with the fine particles (sediment) carried the
greatest distances across the field. Sediment is the principal carrier
of sorbed pesticides. The Universal Soil Loss Equation (USLE) developed
by USDA is a simple method used to determine erosion losses. The USLE
is most accurate for long-term average erosion losses.
The universal soil loss equation used in PRZM is the modification
described by Williams, et al., Transactions ASAE, 20(6), 1977 (see
Section 2 for details). The Williams modification replaces the R (rain-
fall erosivity) term with an energy term. The energy term enables the
estimation of event totals for erosion from the field. The modified
universal soil loss equation (MUSLE) requires the remaining four USLE
factors with no modifications.
TR Peak runoff rate. Total runoff is easily calculated with the
curve number technique, but the problem remains to estimate the
peak runoff rate. Most runoff producing storms occur over a
short duration. The model assumes a trapezoidal hydrograph (see
Section 2) with storm duration (TR) specified as an input. Un-
fortunately, data to estimate TR are not often readily available.
TR is entered as an average, although in reality this para-
meter changes seasonally as well as with individual storm type.
Because most erosion losses occur shortly after plowing or other
tillage prior to crop emergence, the value of TR should be appro-
priate for this period. Several references (Heimstra, L. A. V.
and R.C. Crease. J. Hydrol. 11, 1970.; Grace, R. A. and P.
S. Eagleson. Report No. 91, Mass. Insti. Tech., 1966.; Varas,
E. A. and R. K. Linsley. J. Hydrol. 34, 1977.; Eagleson, P. S.
Water Res. Res. 14(5), 1978.; and Dean, J. D. MS Thesis, Univ.
Ga. 1979.) give representative values of storm duration. Figure
6 provides an estimate of TR for a few locations in the United
States. If more detailed information is desired, representative
storm durations can be estimated from hourly rainfall records.
Soil loss estimates can be adjusted by calibrating this paramter
to match annual soij^loss estimates. The soil loss estimates are
proportional to 1//TR (a four-fold decrease in TR will pro-
duce a two-fold increase in soil loss).
USLEK Soil erodibility factor. USLEK is a soil specific parameter.
Specific values for various soils are obtainable from local Soil
Conservation Service (SCS) offices. Approximate values (based on
broad ranges of soil properties) can be estimated from Table 3.
44
-------
(0
S-i
a
o
x:
w
!—I
cfl
S-i
01
c
•H
O
•H
O
4-)
Cfl
00
•H
•H
+J
cn
Q)
o
4-1
rt
S-i
to]
n)
•H
Q
Ml
ft,
45
-------
Table 3. Indications of the General Magnitude of the
Soil/Erodibility Factor, Ka
Texture class
Sand
Fine sand
Very fine sand
Loamy sand
Loamy fine sand
Loamy very fine sand
Sandy loam
Fine sandy loam
Very fine sandy loam
Loam
Silt loam
Silt
Sandy clay loam
Clay loam
Silty clay loam
Sandy clay
Silty clay
Clay
Organic
<0.5%
K
0.05
.16
.42
.12
.24
.44
.27
.35
.47
.38
.48
.60
.27
.28
.37
.14
.25
Matter Content
2%
K
0.03 0
.14
.36
.10
.20
.38
.24
.30
.41
.34
.42
.52
.25
.25
.32
.13
.23
0.13-0.29
4%
K
.02
.10
.28
.08
.16
.30
.19
.24
.33
.29
.33
.42
.21
.21
.26
.12
.19
aThe values shown are estimated averages of broad ranges of specific-
soil values. When a texture is near the borderline of two texture classes,
use the average of the two K values. For specific soils, Soil Conservation
Service K-value tables will provide much greater accuracy. (Control of
Water Pollution from Cropland, Vol. I, A Manual for Guideline Development.
U.S. Environmental Protection Agency, Athens, GA. EPA-600/2-75-026a.)
46
-------
USLELS Length slope and steepness factor. USLELS is a topographic
parameter and is dimensionless. Values for LS can be estimated
from Table 4.
USLEP Supporting practice factor. USLEP is a conservation supporting
practice parameter and is dimensionless. Values range from
0.10 (extensive practices) to 1.0 (no supporting practice).
Specific values for P can be estimated from Table 5.
USLEC Cover and management factor. USLEC is a management parameter
and is dimensionless. Values range from 0.001 (well managed)
to 1.0 (fallow or tilled condition). One value for each of the
three growing periods (fallow, cropping, residue) are required.
Specific local values can be computed from Agricultural Hand-
book No. 282 (USDA) or obtained from the local SCS office.
Generalized values are provided in Table 6.
4.2.5 Maximum Crop Interception—(CINTCP) Card 7
The crop interception parameter (CINTCP) estimates the amount of
rainfall that is intercepted by a fully developed plant canopy and
retained on the plant surface, cms. A range of 0.1 - 0.3 cm for a dense
crop canopy is reported (29). Values for several major crops are pro-
vided in Table 7.
4.2.6 Active Crop Rooting Depth—(AMXDR) Card 7
PRZM requires input of the maximum active crop rooting depth
(AMXDR), in centimeters, for the simulated crop (or the deepest root
zone of multiple crop simulations) measured from the land surface. Gen-
eralized information for corn, soybeans, wheat, tobacco, grain sorghum,
potatoes, peanuts, and cotton are provided in Table 8. If minor crops,
such as mint, are simulated, or site specific information alters the
generalized information, consulting with USDA Handbook No. 283 (Usual
Planting and Harvesting Dates), or the Cooperative Extension Service in
the specific locale is advisable.
4.2.7 Runoff and Infiltration—(CN) Card 7
The interaction of hydrologic soil group (soil) and land use and
treatment (cover) is accounted for by assigning a runoff curve number
(CN) for average soil moisture condition (AMC II) to important soil
cover complexes for the fallow, cropping, and residue parts of a growing
season3. The average curve numbers for each of the three soil cover
complexes are estimated using Tables 8 through 12. The following steps
provide a procedure for obtaining the correct curve numbers. Corn
planted in straight rows will be used as an example.
aOnce the curve number for AMC II is located, the model calculates the
carve number for AMC I and AMC III. In this way, the set of nine curve
numbers required to describe each crop simulated are provided.
47
-------
Table 4. Values of the Erosion Equation's Topographic Factor, LS,
for Specified Combinations of Slope Length and Steepnessa
Slope Length (feet)
%
Slope
0.5
1
2
3
4
5
6
8
10
12
14
16
18
20
25
30
40
50
25
0.07
0.09
0.13
0.19
0.23
0.27
0.34
0.50
0.69
0.90
1 .2
1 .4
1 .7
2.0
3.0
4.0
6.3
8.9
50
0.08
0.10
0.16
0.23
0.30
0.38
0.48
0.70
0.97
1 .3
1 .6
2.0
2.4
2.9
4.2
5.6
9.0
13.0
75
0.09
0.1 2
0.19
0.26
0.36
0.46
0.58
0.86
1 .2
1 .6
2.0
2.5
3.0
3.5
5.1
6.9
1 1 .0
15.0
100
0.10
0.13
0.20
0.29
0.40
0.54
0.67
0.99
1 .4
1 .8
2.3
2.8
3.4
4.1
5.9
8.0
13.0
18.0
150
0.1 1
0.15
0.23
0.33
0.47
0.66
0.82
1 .2
1 .7
2.2
2.8
3.5
4.2
5.0
7.2
9.7
16.0
22.0
200
0.12
0.16
0.25
0.35
0.53
0.76
0.95
1 .4
1 .9
2.6
3.3
4.0
4.9
5.8
8.3
1 1 .0
18.0
25.0
300
0.14
0.18
0.28
0.40
0.62
0.93
1 .2
1 .7
2.4
3.1
4.0
4.9
6.0
7.0
10.0
14.0
22.0
31 .0
400
0.15
0.20
0.30
0.44
0.70
1 .1
1 .4
2.0
2.7
3.6
4.6
5.7
6.9
8.2
12.0
16.0
25.0
--
500
0.16
0.21
0.33
0.47
0.76
1 .2
1 .5
2.2
3.1
4.0
5.1
6.4
7.7
9.1
13.0
18.0
28.0
—
600
0.17
0.22
0.34
0.49
0.82
1 .3
1 .7
2.4
3.4
4.4
5.6
7.0
8.4
10.0
14.0
20.0
31 .0
--
800
0.19
0.24
0.38
0.54
0.92
1 .4
1 .9
2.8
3.9
5.1
6.5
8.0
9.7
12.0
17.0
23.0
—
--
1000
0.20
0.26
0.40
0.57
1 .0
1 .7
2.1
3.1
4.3
5.7
7.3
9.0
1 1 .0
13.0
19.0
25.0
.__
—
60 12.0 16.0 20.0 23.0 28.0
Values given for slopes longer than 300 feet or steeper than 18% are
extrapolations beyond the range of the research data and, therefore, less
certain than the others. (Control of Water Pollution from Cropland, Vol.
I, A Manual for Guideline Development. U.S. Environmental Protection
Agency, Athens, GA. EPA-600/275-026a.)
48
-------
Table 5. Values of Support-Practice Factor, pa
Land Slope (percent)
Practice
Contouring (Pc)
Contour strip
cropping (P c)
s
R-R-M-M-,
R-W-M-M
R-R-W-M
R-W
R-0
Contour listing or
ridge planting (Pc^)
Contour terracing (Pt)°
No support practice
1 .1-2
0.60
0.30
0.30
0.45
0.52
0.60
0.30
d0.6//n~
1 .0
2.1-7
0.50
0.25
0.25
0.38
0.44
0.50
0.25
0.5/Vn~
1 .0
7.1-12
(Factor
0.60
0.30
0.30
0.45
0.52
0.60
0.30
0.6//n~
1 .0
12.1-18
P)
0.80
0.40
0.40
0.60
0.70
0.80
0.40
0.8//n~
1 .0
18.1-24
0.90
0.45
0.45
0.68
0.90
0.90
0.45
0.9//rT
1 .0
aControl of Water Pollution From Cropland, Vol. I, A Manual for Guide-
line Development, U.S. Environmental Protection Agency, Athens, GA. EPA-
600/2-75-026a.
bR = rowcrop, W = fall-seeded grain, O = spring-seeded grain, M =
meadow. The crops are grown in rotation and so arranged on the field that
rowcrop strips are always separated by a meadow or winter-grain strip.
GThese Pt values estimate the amount of soil eroded to the terrace
channels and are used for conservation planning. For prediction of off-field
sediment, the P^. values are multiplied by 0.2.
^n = number of approximately equal-length intervals into which the
field slope is divided by the terraces. Tillage operations must be parallel
to the terraces.
49
-------
Table 6. Generalized Values of the Cover and Management Factor, C, in
the 37 States East of the Rocky Mountains3'13
Line Crop, Rotation, and Management0
No.
Productivity
Leveld
High
Mod.
C value
Base value: continuous fallow, tilled up and down slope
Corn
1 C, RdR, fall TP, conv (1)
2 C, RdR, spring TP, conv (1)
3 C, RdL, fall TP, conv (1)
4 C, RdR, we seeding, spring TP, conv (1)
5 C, RdL, standing, spring TP, conv (1)
6 C, fall shred stalks, spring TP, conv (1)
7 C(silage)-W(RdL, fall TP) (2)
8 C, RdL, fall chisel, spring disk, 40-30% re (1)
9 C(silage), W we seeding, no-till p1 in c-k W (1)
10 C(RdL)-W(RdL, spring TP) (2)
11 C, fall shred stalks, chisel p1 , 40-30% re ( 1 )
12 C-C-C-W-M, Rdl, TP for C, disk for W (5)
13 C, RdL, strip till row zones, 55-40% re (1)
14 C-C-C-W-M-M, RdL, TP for C, disk for W (6)
15 C-C-W-M, RdL, TP for C, disk for W (4)
16 C, fall shred, no-till p1 , 70-50% re ( 1 )
17 C-C-W-M-M, RdL, TP for C, disk for W (5)
18 C-C-C-W-M, RdL, no-till p1 2d & 3rd C (5)
19 C-C-W-M, RdL, no-till p1 2d C (4)
20 C, no-till p1 in c-k wheat, 90-70% re (1)
21 C-C-C-W-M-M, no-till p1 2d & 3rd C (6)
22 C-W-M, RdL, TP for C, disk for W (3)
23 C-C-W-M-M, RdL, no-till p1 2d C (5)
24 C-W-M-M, RdL, TP for C, disk for W (4)
25 C-W-M-M-M, RdL, TP for C, disk for W (5)
26 C, no-till p1 in c-k sod, 95-80% re (1)
Cotton6
27 Cot, conv (Western Plains) (1)
28 Cot, conv (South) (1)
Meadow
29 Grass & Legume mix
30 Alfalfa, lespedeza or Sericia
31 Sweet clover
1 .00
0.54
.50
.42
.40
.38
.35
.31
.24
.20
.20
.19
.17
.16
.14
.12
.11
.087
.076
.068
.062
.061
.055
.051
.039
.032
.017
0.42
.34
0.004
.020
.025
1 .00
0.62
.59
.52
.49
.48
.44
.35
.30
.24
.28
.26
.23
.24
.20
.17
.18
.14
.13
.1 1
.14
.1 1
.095
.094
.074
.061
.053
0.49
.40
0.01
50
-------
Table 6. Generalized Values of the Cover and Management Factor, C, in
the 37 States East of the Rocky Mountainsa'b (Continued)
Line Productivity
No. Crop, Rotation, and Management0 Level^
High
Mod.
C value
Base value: continuous fallow, tilled up and down slope 1 .00
Sorghum, grain (Western Plains)6
32 RdL, spring TP, conv (1) 0.43
33 No-till p1 in shredded 70-50% re .11
Soybeans6
34 B, RdL, spring TP, conv (1) 0.48
35 C-B, TP annually, conv (2) .43
36 B, no-till p1 .22
37 C-B, no-till p1 , fall shred C stalks (2) .18
Wheat
38 W-F, fall TP after W (2) 0.38
39 W-F, stubble mulch, 500 Ibs re (2) .32
40 W-F, stubble mulch, 1000 Ibs re (2) .21
41 Spring W, RdL, Sept TP, conv (N & S Dak) (1) .23
42 Winter W, RdL, Aug TP, conv (Kans) (1) .19
43 Spring W, stubble mulch, 750 Ibs re ( 1 ) .15
44 Spring W, stubble mulch, 1250 Ibs re ( 1 ) .12
45 Winter W, stubble mulch, 750 Ibs re (1) .11
46 Winter W, stubble mulch, 1250 Ibs re ( 1 ) .10
47 W-M, conv (2) .054
48 W-M-M, conv (3) .026
49 w-M-M-M, conv (4) .021
1 .00
0.53
.18
0.54
.51
.28
.22
aThis table is for illustrative purposes only and is not a complete
list of cropping systems or potential practices. Values of C differ with
rainfall pattern and planting dates. These generalized values show approx-
imately the relative erosion-reducing effectiveness of various crop systems,
but locationally derived C values should be used for conservation planning
at the field level. Tables of local values are available from the Soil
Conservation Service.
bControl of Water Pollution from Cropland, Vol. I, A Manual for Guide-
line Development. U.S. Environmental Protection AGency, Athens, GA.
EPA-600/3-75-026a.
GNumbers in parentheses indicate number of years in the rotation
cycle. No. (1) designates a continuous one-crop system.
level is exemplified by long-term yield averages greater than
75 bu. corn or 3 tons grass-and-legume hay; or cotton management that
regularly provides good stands and growth.
51
-------
Table 6. Generalized Values of the Cover and Management Factor, C, in
the 37 States East of the Rocky Mountains3'b (Continued)
eGrain sorghum, soybeans, or cotton may be substituted for corn in
lines 12, 14, 15, 17-19, 21-25 to estimate C values for sod-based rotations
Abbreviations defined:
B - soybeans
C - corn
c-k - chemically killed
conv - conventional
cot - cotton
F - fallow
M - grass & legume hay
pi - plant
W - wheat
we - winter cover
Ibs re - pounds of crop residue per acre remaining on surface after new
crop seeding
% re - percentage of soil surface covered by residue mulch after new
crop seeding
70-50% re - 70% cover for C values in first column; 50% for second column
RdR - residues (corn stover, straw, etc.) removed or burned
RdL - all residues left on field (on surface or incorporated)
TP - turn plowed (upper 5 or more inches of soil inverted, covering
residues)
52
-------
Table 7. Interception Storage for Major Crops
Crop
Density
CINTCP (cm)
Corn
Soybeans
Wheat
Oats
Barley
Potatoes
Peanuts
Cotton
Tobacco
Heavy
Moderate
Light
Light
Light
Light
Light
Moderate
Moderate
0.25 - 0.30
0.20 - 0.25
0.0 - 0.15
0.0 - 0.15
0.0 - 0.15
0.0 - 0.15
0.0 - 0.15
0.20 - 0.25
0.20 - 0.25
53
-------
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Step 1. From Appendix B find the hydrologic soil group for the
particular soil that is in the area under consideration10.
There are four different soil classifications (A, B, C,
D) and are in the order of decreasing percolation poten-
tial and increasing slope and runoff potential. Soil
characteristics associated with each hydrologic group are
as follows0.
Group A: Deep sand, deep loess, aggregated silts, mini-
mum infiltration of 0.76 - 1.14 (cm hr~1) .
Group B: Shallow loess, sandy loam, minimum infiltration
0.38 - 0.76 (cm hr~1).
Group C: Clay loams, shallow sandy loam, soils low in
organic content, and soils usually high in clay,
minimum infiltration 0.13 - 0.38 (cm hr~1).
Group D: Soils that swell significantly when wet, heavy
plastic clays, and certain saline soils, mini-
mum infiltration 0.03 - 0.13 (cm hr~1).
If the soil series or soil properties are not known,
the hydrologic soil group can be estimated from Figure 7.
Care must be exercised, however, in use of this fig-
ure. Considerable spatial aggregation was made in order to
develop the generalized map over such a large area.
Where possible development of more highly resolved data
is preferable.
Step 2. From Table 9 find the land use and treatment or practice
that is to be simulated (e.g., row crops, straight row).
Step 3. From Table 9 find the hydrologic condition of the soil
that is to be simulated (e.g., good).
Step 4. From Table 9 find the curve number for antecedent mois-
ture condition II for the site selected. Example:
Hydrologic group = A, treatment practice is straight row,
land use is row crops, hydrologic condition is good. The
curve number for the cropping season is 67.
Step 5. Follow the same procedure for the fallow portion of the
growing season using only the hydrologic soil group.
^Appendix B contains a listing of soil groups and their hydrologic soi
1 cover classification.
CA Guide to Hydrologic Analysis using SCS Methods. 1982. Richard H.
McCuen. Prentice Hall.
55
-------
Table 9. Runoff Curve Numbers for Hydrologic Soil-Cover Complexesa
(Antecedent Moisture Condition II, and Ia = 0.2 S)
Land Use
Fallow
Row crops
Small
grain
Close-
seeded
legume s^
or rota-
tion
meadow
Pasture
or range
Meadow
Woods
Farmsteads
Roads
(dirt)c
Cover
Treatment
or Practice
Straight row
Straight row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced
Straight row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced
Straight row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced
Contoured
Contoured
Contoured
Hydrologic
Condition
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Fair
Good
Poor
Fair
Good
Good
Poor
Fair
Good
Hydrologic
A
77
72
67
70
65
66
62
65
63
63
61
61
59
66
58
64
55
63
51
68
49
39
47
25
6
30
45
36
25
59
72
74
B
86
78
78
79
75
74
71
76
75
74
73
72
70
77
72
75
69
73
67
79
69
61
67
59
35
58
66
60
55
74
82
84
Soil
C
91
85
85
84
82
80
78
84
83
82
81
79
78
85
81
83
78
80
76
86
79
74
81
75
70
71
77
73
70
82
87
90
Group
D
94
91
89
88
86
82
81
88
87
85
84
82
81
89
85
85
83
83
80
89
84
80
88
83
79
78
83
79
77
86
89
92
(hard surface)0
aSoil Conservation Service, USDA.
Section 4, Hydrology. 1971.
^Close-drilled or broadcast.
^Including right-of-way.
SCS National Engineering Handbook,
56
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Table 10. Method for Converting Crop Yields to Residue3
Cropb
Barley
Corn
Oats
Rice
Rye
Sorghum
Soybeans
Winter wheat
Spring wheat
Straw/Grain
Ratio
1 .5
1 .0
2.0
1 .5
1 .5
1 .0
1 .5
1 .7
1 .3
Bushel
Weight
(Ibs)
48
56
32
45
56
56
60
60
60
aCrop residue = (straw/grain ratio) x (bushel weight in Ib/bu) x (crop
yield in bu/acre).
bKnisel, W. G. (Ed.). CREAMS: A Field-Scale Model for Chemicals,
Runoff, and Erosion from Agricultural Management Systems. USDA, Conservation
Research Report No. 26, 1980.
Table 11. Residue Remaining From Tillage Operations9
Tillageb Residue
Operation Remaining
Chisel plow 65
Rod weeder 90
Light disk 70
Heavy disk 30
Moldboard plow 1 0
Till plant 80
Fluted coulter 90
V Sweep 90
aCrop residue remaining = (crop residue from Table 10) x (tillage
factor(s)) .
bKnisel, W. G. (Ed.). CREAMS: A Field-Scale Model for Chemicals,
Runoff, and Erosion from Agricultural Management Systems. USDA, Conservation
Research Report No. 26, 1980.
57
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Table 12. Reduction in Runoff Curve Numbers Caused by Conservation
Tillage and Residue Management3
Large
Residue
Crop13
(Ib/acre)
0
400
700
1 ,100
1 ,500
2,000
2,500
6,200
Medium
Residue
Cropc
(Ib/acre)
0
150
300
450
700
950
1 ,200
3,500
Surface
Covered
by Residue
(%)
0
10
19
28
37
46
55
90
Reduction
in Curve
Number^
(%)
0
0
2
4
6
8
10
10
aKnisel, W. G. (Ed.). CREAMS: A Field-Scale Model for Chemicals,
Runoff, and Erosion from Agricultural Management Systems. USDA, Conservation
Research Report No. 26, 1980.
'-'Large-residue crop (corn) .
cMedium residue crop (wheat, oats, barley, rye, sorghum, soybeans).
^Percent reduction in curve numbers can be interpolated linearly. Only
apply 0 to 1/2 of these percent reductions to CN's for contouring and terrac-
ing practices when they are used in conjunction with conservation tillage.
58
-------
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Example: Hydrologic soil group A, land use fallow, curve
number for condition II is 77,
Step 6. The post-harvest or residue portions of the year requires
curve numbers that reflect the extent of surface cover
after harvest. This can be quite variable and in many
cases may require considerable judgement. Under "average"
conditions a value set to the mean of the fallow and
growing period numbers (from steps 4, 5) is appropriate.
In the example case, this number will be the mean of 77
and 67, or 72.
Step 7. The curve number input sequence is now written as
77 67 72
Additional guidance for management practices
Pesticides are being increasingly used in conjunction
with conservation practices to reduce erosion and runoff.
Most notable among these practices is the use of conser-
vation tillage. The idea is to increase the soil surface
residue and hence reduce erosion and runoff by increasing
infiltration. The curve numbers developed in steps 1-7
assume conventional practices and must be further modified
to reflect the changes in management. Both the fallow
and growing season numbers must be modified. For purposes
of this example, assume the corn is produced by using
chisel plows rather than the conventional tillage assumed
above. The following steps now apply.
Step 8. From Table 10 find the straw/grain ratio for corn, which
is 1 .0.
Step 9. From Table 10 find the bushel weight of corn, which is 56.
-Step 10. From Table 8 find bushel/acre yield of corn, which is 110.
Step 11. Multiply straw/grain ratio * bushel weight * bushel
weight/acre = crop residue produced by the crop. For
corn, 1.0 x 56 x 110 = 6160.
Step 12. From Table 11 find the tillage practice desired for the
crop use site (e.g. chisel plow).
Step 13. Multiply the crop residue determined in step 11 by the
tillage factor from step 12 to determine residue remain-
ing, i.e, 6160 x 0.65 = 4004.
60
-------
Step 14. From Table 12 find the reduction in curve number for AMC
II, crop curve number produced from residue remaining
after harvest determined in step 12. For corn at 4000
pounds per acre, a 10% reduction in curve number is
produced.
Step 15. Determine the curve number for antecedent moisture
condition (AMC) II. From Steps 1-5, AMC II was 67.
67 * 0.10 = 6.7, which is rounded to 7.0. The modified
curve numbers are 67 - 7 = 60 and 77 - 7 = 70.
Step 16. The post-harvest curve number must also now be reduced by
averaging the fallow and growing season numbers, that is,
70 and 60 to yield 65.
4.2.8 Maximum Areal Coverage—(COVMAX) Card 7
If the user chooses to proportion the applied pesticide between the
plant canopy and the soil surface as a linear function of the ground
cover (see FAM parameter, Section 3), then the model estimates the
ground cover as the crop grows to some maximum value, COVMAX. The
maximum areal coverage (COVMAX) afforded by crop determines the fraction
of ground cover afforded by the crop and thus influences the mass of
pesticide that reaches the ground from application. Very little infor-
mation is available on maximum areal coverage. Williams (Pesticide
Runoff Simulator, EPA Contract No. 68-01-3840, Office of Pesticide
Programs, 1980) has related the fraction of ground cover to the leaf
area index of the crop. The ground cover afforded by the crop is esti-
mated with the equation
COVMAX = (2. - ERFC (1.33 LAI1>m - 2.))/2.1 (32)
where COVMAX = fraction of ground covered by the plant
LAI = leaf area index of crop, m, on day, 1
ERFC = complimentary error function
4.2.9 Maximum Foliar Dry Weight—(WFMAX) Card 7
If the user chooses to have the model estimate the distribution
between plants and the soil by an exponential function, then WFMAX must
be specified.
The maximum foliar dry weight, WFMAX, of the plant above ground
(kg m~2) is the exponent used in the exponential foliar pesticide appli-
ca tion model. WFMAX can be estimated using Table 13. Estimates for
other crops will require yield information that is available from from
USDA crop reporting service.
61
-------
Table 13. Values for Estimating WFMAX in Exponential Foliar Model
Crop
Corn
Sorghum
Soybeans
Winter
wheat
Yielda
(Bu/Ac)
1 10
62
35
40
Bushela
dry wt.
(Ibs/Bu)
56
56
60
60
Straw/Grain
Ratio
1 .0
1 .0
1 .5
1 .7
Units
Conversion
Factor
1 .1214 x 10~4
1 .1214 x 10~4
1 .1214 x 10-4
1 .1214 x 10~4
WFMAX
1 .38
0.78
0.59
0.72
a10-year average.
WFMAX is computed by finding the product of columns 2, 3, and 5,
and by multipling this number by the straw/grain ratio (col. 4) plus
1.0. The straw/grain ratio defines the amount of straw associated with
the final grain product. Both the straw and grain should be accounted
for to determine the maximum weight. Thus, the straw-to-grain ratio
should have (1 .0) added to it when used to compute WFMAX. An example is
provided for barley.
Step 1. Yield, bushel dry wt., and straw/grain ratio for barley
are 42.0, 48.0, and 1.5, respectively.
Step 2. WFMAX = Bu/Ac * Lbs/Bu * (straw/grain ratio + 1.)
* conversion factor to yield (kg m~2) for PRZM input.
Step 3. Conversion factor = 2.47 Ac_ * 1 ha * 0.454 kg =
ha 104m2 Lbs
1 .1214 x 10~4.
Step 4. WFMAX = 42.0 * 48.0 * (1.5 + 1.0) * 1.1214 x 10~4,
which equals 0.56.
4.2.9.1 Cropping Information for Emergence, Maturity, and Harvest—(BMP,
EMM, IYREM, MAD, MAM, IYRMAT, HAD, HAM, IYRHAR) Card 9
Generalized cropping information including date of emergence (EMD,
EMM, IYREM), maturity (MAD, MAM, IYRMAT), and harvest (HAD, HAM, IYRHAR)
for eight major crops including corn, soybeans, wheat, tobacco, grain
sorghum, potatoes, and peanuts are provided in Table 8. Simulations in-
volving minor crops such as mint, or where site specific information al-
ters the general practices provided, may require consultation with USDA
62
-------
Handbook No. 283 (Usual Planting and Harvesting Dates) or the local
Cooperative Extension Service.
4.3 PESTICIDES
Pesticides can be applied directly to the soil surface, the plant
canopy, or to both. Two modeling problems arise when one considers this.
First, the initial distribution of the applied pesticide between plant
foliage and the soil surface must be estimated. Second, the remaining
foliar deposited pesticides then become available for degradation (photo-
lysis) or removal (volatilization, washoff). Recall from Section 3 that
two options are available for how one chooses to distribute the applied
pesticides (the FAM parameter).
4.3.1 Initial Foliage to Soil Distribution—(FILTRA) Card 1 3A
The filtration parameter (FILTRA) relates to the equation for parti-
tioning the applied pesticide between the foliage and ground (this ap-
plies when FAM = 3). Lassey, K. R. Atmospheric Environment 16(1), 1982,
suggest values in the range of 2.3 - 3.3 m2 kg"1.Miller, C. W. in Pro-
ceedings of Symposium, Biological Implications of Radionuclides Released
from Nuclear Industries, Vol II, Vienna, 1979, suggested a value of 2.8
m^ kg~1 for pasture grasses. Most of the variation appears to be due to
the vegetation and not the aerosol.
4.3.2 Foliar Washoff Flux—(FEXTRC) Card 1 3A
Washoff from plant surfaces is modeled using a relationship among
rainfall, foliar fraction of applied pesticide, and an extraction coeffi-
cient. The parameter (FEXTRC) is the required input parameter to esti-
mate the flux of pesticide washoff. Exact values are varied and depend
upon the crop, pesticide properties, and application method. Smith and
Carsel (46) suggest 0.10 is suitable for most pesticides.
4.3.3 Foliar Disappearance Rate Constant—(PLDKRT) Card 13A
The degradation of pesticides on plant surfaces is modeled by a
simple first-order rate expression. This is a very chemical specific
parameter that must be measured. Typical values for selected pesticides
are provided in Table 14.
4.3.4 Pesticide Soil-Water Distribution Coefficients
The user can enter directly the distribution coefficient or the
model will calculate a value given other pesticide properties. If the
parameter KDFLAG is set to a value of p (CARD 15), then direct data
input is made as the parameter KD (CARD 17A). If KDFLAG is set to 1,
however, additional information is required.
63
-------
Table 14. Degradation Rate Constants of Selected Pesticides on Foliage3
Class
Group
Decay Rate(days
Organochlorine
Organophosphate
Fast
(aldrin, dieldrin, ethylan,
heptachlor, lindane,
methoxychlor).
Slow
(chlordane, DDT, endrin,
toxaphene).
Fast
(acephate, chlorpyrifos-methyl,
cyanophenphos, diazinon, dipterex,
ethion, fenitrothion, leptophos,
malathion, methidathion, methyl
parathion, phorate, phosdrin,
phosphamidon, quinalphos, alithion,
tokuthion, triazophos, trithion).
Slow
(azinphosmethyl, demeton, dimethoate,
EPN, phosalone).
0.231 - 0.1386
0.1195 - 0.0510
0.2772 - 0.3013
0.1925 - 0.0541
Carbamate
Pyrethroid
Pyridine
Benzoic acid
Fast
(carbof uran)
Slow
(carbaryl)
(permethrin)
(pichloram)
(dicamba)
0.630
0.1 260 - 0.0855
0.0196
0.0866
0.0745
aKnisel, W. G. (Ed.). CREAMS: A Field-Scale Model for Chemicals,
Runoff, and Erosion from Agricultural Management Systems. USDA, Conserva-
tion Research Report No. 26, 1980.
64
-------
4.3.5 Options for Use in Estimating Distribution Coefficients from Related
Input Data—(PCMC, SOL) Card 15A
The fate of pesticides in soil and water is highly dependent on
the sorptive characteristics of the compound. Sorptive characteristics
affect the physical movement of pesticides significantly. The sorptive
properties of pesticides generally correlate well with the organic carbon
content of soils. The carbon content of most soils decreases with depth.
The PRZM model allows for estimating the partition coefficient for
pesticides with depth from one of three models (8, 27, 28) based on
water solubility and corrected for organic carbon. The three models
are:
PCMC1 Log Koc = (-0.54 * Log SOL) + 0.44 (35)
where KOC = organic carbon distribution coefficient
SOL = water solubility, mole fraction
PCMC2 Log KQC = 3.64 - (0.55 * Log SOL) (36)
where SOL = water solubility, milligrams liter""1
PCMC3 Log KQC = 4.40 - (0.557 * Log SOL) (37)
where SOL = water solubility, micro moles liter"1
These models are selected by setting PCMC to values of 1, 2, or 3,
respectively. These methods were selected because of referenced documen-
tation and provisions for direct use with the most commonly reported
physical pesticide parameter, water solubility. The three models used
in PRZM for estimating partitioning between soil and water are limited
to specific types of pesticides. These equations are best used for
pesticides having melting points below 120 °C. Solubilities above these
temperatures are affected by crystalline energy and other such physical
properties. The three models are not appropriate for pesticides whose
solubilities are affected by crystalline energy or other physical proper-
ties, and would have a tendency to overestimate the partitioning between
soil and water. Of the three models, the first model is for true equili-
brium of completely dispersed particles of soil/water concentrations
less than 10.0 g I"1. The second and third models are for soil/water
concentrations greater than 10.0 g 1~1 and for short equilibrium periods
of 48 hours or less. For most PRZM applications, the first model would
be appropriate.
Selected pesticides having properties amenable for use with the
water solubility models are provided in Table 15.
The pesticide solubility, SOL, must also be input. Units must be
consistent with the model chosen. Table 15 provides pertinent units for
the selected pesticides.
65
-------
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4.3.6 User Specified Distribution Coefficients—(KD) Card 17A
A useful relationship exists between the octanol-water distribution
coefficient and the organic carbon distribution coefficient. This
relationship can be used when measured soil distribution coefficients
are not available, or the pesticides posses crystalline energy proper-
ties that would preclude the use of any water solubility models.
The octanol-water distribution coefficient can be used for calcu-
lating distribution coefficients for pesticides that posses monomer-
associated properties for solubility in water. Karickhoff et al. Water
Res. 13, 1979, proposed a relationship to KQW by
log Koc = 1.00 (log KQW) - 0.21 (33)
where KQW = octanol-water distribution coefficient
KQC = organic carbon distribution coefficient
Carbofuran is a pesticide that exhibits crystalline energy relation-
ships and its apparent distribution coefficient should be estimated using
its log KQW, which is 2.44. Substituting into the Karickhoff equation
log Koc = 1.00 (2.44) - 0.21 = 2.23
KQC = 102'23 = 169.8
For a soil with 0.5% organic carbon the K^ of the pesticide is
Kd = Koc (percent organic carbon) (34)
Too
Kd = 169.8 (0.5) = 0.85
100
This compares to an estimated K^ of 2.68 using the PCMC1 water solubility
model (Card 15A). Selected pesticides having properties amenable for use
with the octanol water distribution model by Karickhoff are provided in
Table 16.
4.3.7 Degradation Rate Constants—(DKRATE) Card 17
The processes that contribute to pesticide disappearance in soils
are varied and depend on environmental factors as well as chemical
properties. Unfortunately, only rarely are process-specific rate con-
stants (e.g., hydrolysis) reported for the soil environment. In most
cases, a lumped first-order rate constant is assumed. This is the model
used in PRZM. Although such an approximation is imprecise, most modeling
efforts follow the same approach and many pesticides appear to behave
similarly. For example, Nash (36) found that disappearance of many
compounds was highly correlated to a first order approximation with r2 >
0.80. More recently, Rao, et al., 1984 (Estimation of Parameters for
Modeling The Behavior of Selected Pesticides and Orthophosphate, EPA-
71
-------
Table 16. Octanol Water Distribution Coefficients (log Knw) and
Soil Degradation Rate Constants for Selected Chemicals
Chemical Name
Alachlor
Aldicarb
Altos id
Atrazine
Benomyl
Bif enox
Bromacil
Captan
Carbaryl
Carbof uran
Chloramben
Chlordane
Chloroacetic Acid
Chloropropham
Chloropyrifos
Cyanazine
Dalapon
Dialifor
Diazinon
Dicamba
Dichlobenil
Dichlorofenthion
2 , 4-Dichlorophenoxy-
acetic Acid
Dichloropropene
Diciofol
Dinoseb
Diuron
Endrin
Fenitrothion
Fluometuron
Linuron
Malathion
Me thomyl
Me thoxychlor
Methyl Parathion
Monolinuron
Monuron
MS MA
Nitrofen
Parathion
Permethrin
Phorate
Phosalone
b
Log Kow
2.78
0.70
2.25
2.45
2.42
2.24
2.02
2.35
2.56
2.44
1 .1 1
4.47
-0.39
3.06
4.97
2.24
0.76
4.69
3.02
0.48
2.90
5.14
2.81
1 .73
3.54
2.30
2.81
3.21
3.36
1 .34
2.19
2.89
0.69
5.08
3.32
1 .60
2.12
-3.10
3.10
3.81
2.88
2.92
4.30
Degradation Rate
Constant (days"1 )
0.0384
0.0322 -
0.0149 -
0.1486 -
0.1420
0.1 196 -
0.0768 -
0.0020 -
0.0058 -
0.0495
0.0462 -
0.0330 -
0.2140 -
0.0116 -
0.0693 -
0.0462 -
0.0035 -
0.1 155 -
0.0231
0.0280 -
02.91 -
0.0046 -
0.2207
0.0046 -
0.2961 -
0.0396
0.0363 -
0.0116
0.0063
0.0023
0.0768
0.0079
0.0007
0.00267
0.0231
0.0067
0.0197
0.0039
0.0231
0.0231
0.0014
0.0578
0.0039
0.4152
0.0033
0.0020
0.0046
0.0040
Reference
a
a
a
a
a
a
a
d
c
d
a
a
d
d
d
a
c
a
a
a
a
d
a
e
a
72
-------
Table 16. Octanol Water Distribution Coefficients (log Knw) and
ow'
Soil Degradation Rate Constants for Selected Chemicals
(Continued)
Chemical Name
Log Kow
Degradation Rate
Constant (days'1)
Reference
Phosmet
Picloram
Propachlor
Propanil
Propazine
Propoxur
Ronnel
Simazine
Terbacil
Terbufos
Toxaphene
Trif luralin
Zineb
2.83
0.30
1 .61
2.03
2.94
1 .45
4.88
1 .94
1 .89
2.22
3.27
4.75
1 .78
0.0354
0.0231
0.693
0.0035
0.0539
0.0046
0.0956
0.0512
- 0.0019
- 0.0139
- 0.231
- 0.0017
- 0074
- 0.0026
a
d
d
d
a
e
a
a
aNash, R. G. 1980. Dissipation Rate of Pesticides from Soils.
Chapter 17. _ItJ CREAMS: A Field Scale Model for Chemicals, Runoff, and
Erosion from Agricultural Management Systems. W. G. Knisel, ed. USDA
Conservation Research Report No. 26. 643pp.
Smith, C. N. Partition Coefficients (Log Knw) for Selected Chemicals.
•ow
Athens Environmental Research Laboratory, Athens, GA.
1981 .
Unpublished report,
ed.
GHerbicide Handbook of the Weed Science Society of America, 4th
1979.
"^Control of Water Pollution from Cropland, Vol. I, a manual for
guideline development, EPA-600/2-75-026a.
eSmith, C. N. and R. F. Carsel. Foliar Washoff of Pesticides (FWOP)
Model: Development and Evaluation. Accepted for publishing in Journal of
Environmental Science and Health - Part B. Pesticides, Food Contaminants,
and Agricultural Wastes, B 19(3), 1984.
73
-------
600/3-84-019) reported that pesticide disappearance rate constants in
surface horizons of soils (root zone) are reasonably constant across
soils. This is encouraging from a modeling standpoint because of the
decrease in sensitivity testing required for dissipation rates.
The dissipation rate of pesticides below the root zone, however, is
virtually unknown. Several studies have suggested the rate of dissipa-
tion decreases with depth; however, no uniform correction factor was
suggested between surface/sub-surface rates. First order dissipation
rates for selected pesticides in the root zone are tabulated in Tables
15 and 16.
4.3.8 Plant Uptake of Pesticides—(UPTKF) Card 15
The plant uptake efficiency factor (UPTKF) provides for removal of
pesticides by plants and is a function of the crop root zone and the
interaction of water and chemical properties of the pesticides,, Several
approaches to modeling the uptake of nutrients/pesticides have been pro-
posed ranging from process models that treat the root system as a
distribution sink of known density or strength to empirical approaches
that assume a relationship to the transpiration rate. To obtain informa-
tion on the actual mass of residue removed by the plant, both the concen-
tration of the pesticide and the mass of the plant tissue are required.
Unfortunately most studies of plant uptake do not provide the two consti-
tuents required for calculation of the mass removed. Dejonckheere, W.
et al. Pesti. Sci., 14, 1983, reported the mass of uptake into sugar
beets for the pesticides aldicarb and thiofanox for three soils (sandy
loam, silt loam, and sandy clay loam). Mass removal expressed as a
percentage of applied material for aldicarb on sandy loam, silt loam,
and clay loam ranged from 0.46-7.14%, 0.68 - 2.32%, and 0.15 - 0.74%,
respectively. For thiofanox, 2.78 - 20.22%, 0.81 - 8.70%, and 0.24 -
2.42% removals were reported for the respective soils. The amount of
uptake was higher for sandy soils and increased with available water.
Other reviews have suggested ranges from 4 - 20% for removal by plants
(23), (37).
The procedure adopted for PRZM estimates the removal of pesticides
by plant uptake based on the assumption that uptake of the pesticide is
directly related to the transpiration rate. Sensitivity tests conducted
indicate an increase in the uptake by plants as the root zone depth
increases, and a decrease as the partition coefficient increases. For
highly soluble pesticides and for crop root zones less than 120 cm, the
modeled uptake varied within the range reported by Dejonckheere, et al.
For highly soluble pesticides and for crop root zones of greater than
120 cm, values of. greater than 20% were simulated. For initial esti-
mates a value of 1.0 for UPTKF is recommended. If more than 20 - 25% of
the pesticide is simulated (to be removed by plant uptake), UPTKF should
be calibrated to a value less than 1.0. The uptake efficiency factor is
estimated using a procedure from Briggs et al. Pesti. Sci., 13, 1982.
According to Briggs, the plant uptake efficiency of pesticides can be
described using the equation
74
-------
UPTKF = 0.784 exp - [(log K ,, - 1.78)2/2.44] (38)
ow
where UPTKF = plant uptake efficiency factor
KQW = octanol-water distribution coefficient
The uptake efficiency factor UPTKF using the above equation will vary
from 0.01-0.80 depending on the pesticides partitioning capacity. The
estimated plant uptake efficiency factor is used to calibrate plant
uptake of pesticides when required.
4.3.9 Dispersion—(DISP) Card 17
The dispersion or "smearing out" of the pesticide as it moves down
in the soil profile is attributed to a combination of molecular diffu-
sion and hydrodynamic dispersion. The transport equations solved in
PRZM also produce truncation error leading to a purely mathematical or
numerical dispersion. The terms dropped from the Taylor's series expan-
sion from which the finite difference equations were formulated lead to
errors that appear identical to the intentional expressions for hydro-
dynamic dispersion. For these reasons the DISP parameter must be eval-
uated in light of both "real" and "numerical" components.
Molecular diffusion, Dm, in soils will be lower than free-water
diffusion and has been estimated by Bresler, Water Res. Res. 9(4), 1973,
as
Dm = Dw aebe (39)
p — 1
where D = molecular diffusion in free water, cm day
a = soil constants having a range of 0.001 to 0.005
b = soil constant having an approximate value of 10
9 = volumetric water content, cm-^ cm~3
The free-water diffusion coefficient, Dw, can be estimated from proce-
dures outlined by Lyman et al., Research and Development of Methods for
Estimating Physicochemical Properties of Organic Compounds of Environ-
mental Concern, U.S. Army Medical Research and Development Command Con-
tract DAMD 17-78-C-8073, 1981. In any case, values are quite low,
typically less than 10~6 cm2 day"1, and can be ignored.
Hydrodynamic dispersion is more difficult to estimate because of
its site-soil specificity and its apparent strong dependence upon water
velocity. Most investigators have established an effective diffusion or
dispersion coefficient that combines both molecular and hydrodynamic
terms. This combined expression can then be related to system variables
by developing expressions from field measurements. Most notable among
these expressions is
D = 0.6 + 2.93 v1•11 (40)
75
-------
where D = effective dispersion coefficient, cm2 day"1
v = pore water velocity, cm day"1
by Biggar and Nielsen, Water Res. Res. 12, 1976. Note in Equation 40
that D is now a time and depth varying function since v is both time and
depth-varying. The problem remains to estimate the assumed constant
value for DISP, the PRZM effective dispersion coefficient.
As previously noted, the numerical scheme chosen for solution of
the transport equation produces numerical dispersion. Indeed, this
dispersion is also related to the magnitude of the velocity term. Other
variables that influence the truncation error include the time and space
steps. Because this dispersion is a function of velocity it is not
possible to illustrate the entire range for all anticipated modeling
problems. A sensitivity analysis was performed, however, to examine the
influence of the spatial step, Ax. Results are given in Figure 8.
For these runs the DISP parameter was set to 0.0.
The influence of the DISP parameter superimposed on the numerical
dispersion created by the model at a Ax value of 5.0 cm is shown in
Figure 9. Clearly, even when moderate values for DISP are used,
substantial dispersion is produced. If equation 40 is used along with
typical simulated values for velocity (0.1 - 22 cm day"1), then calcu-
lated DISP values range from 0.83 - 91 cm2 day"1. It is clear -that if
this procedure is used, the desired dispersion will be substantially
higher than it should be because of the model's numerical dispersion.
A number of modeling studies were performed to investigate the
impact of model parameters other than DISP on the apparent dispersion.
From these rather exhaustive studies, the following guidance is offered.
(1) A spatial step or compartment size of 5.0 cm will mimic
observed field effective dispersion quite well and should be
used as an initial value.
(2) No fewer than 30 compartments should be used in order to
minimize mass balance errors created by numerical dispersion.
(3) The DISP parameter should be set to 0.0 unless field data are
available for calibration.
(4) If DISP calibration is attempted, the compartment size should
be reduced to 1.0 cm to minimize the numerical dispersion.
(5) Equation 37 can be used to bound the values only should the
need arise to increase dispersion beyond that produced by the
numerical scheme.
76
-------
X
P.
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o
in
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78
-------
4.3.9.1 Pesticide Application—(APD, APM, IAPYR, DEPI) Card 12
The use of PRZM requires the establishment of a pesticide applica-
tion procedure. This procedure should be somewhat standardized for the
application and distribution forms of a pesticide to minimize the occur-
rence or likelihood of bias in the selection of pesticide application.
The user should follow four steps in establishing a representative
application procedure: (1) establish an application period window
covering the range of possible application dates; '(2) adjust the appli-
cation dates within the window so that application does not occur on a
day immediately before, during, or immediately after a rainfall event
(Pesticides should not be applied to a given field with high moisture
content or for conditions where the efficacy would be diminished.); (3)
select the pesticide mode of application—either aerial or ground sprayer
(pre-plant incorporated or pre-plant not incorporated, post emergence
and/or foliar)--and the distribution in the soil (surface and/or upper
zone); and (4) enter the data for application/distribution into the
proper PRZM input file sequence for DEPI. The work by Donigian, A. S.,
Jr., et al., 1983, (HSPF Parameter Adjustments to Evaluate the Effects
of Agricultural Best Management Practices, EPA-600/3-84-066), provides
guidance on application methods and soil distribution. An outline of
the methodology is provided in Table 17.
4.4 SOILS
The amount of available moisture in the soil is affected by such
properties as temperature and humidity, soil texture and structure,
organic matter content, and plant characteristics (rooting depth and
stage of growth). The moisture content in a soil after "gravity drain-
age" has ceased is known as field capacity. The moisture content in a
soil at which plant survival cannot be achieved and the plant permanent-
ly wilts is called the wilting point. The wilting point, which varies
among specific soils is influenced by colloidal material and organic
matter, but most soils will have a similar wilting point for all common
plants.
Soils have a given volume that is unfilled with solid matter and
is termed pore space. The proportion of pore space is a function of
both the texture and structure of soil (pore space exists between soil
grains and aggregates). The amount of pore space is expressed as a
fraction, cm-^ cm~3f of the total soil volume. The amount of pore
space can vary from horizon to horizon along with other related proper-
ties such as bulk density, field capacity, and field saturation. A
soil whose pores are essentially filled with water is considered satu-
rated although it is virtually impossible to fill literally every pore
in the soil with water. Some residual pore space remains under satu-
rated conditions.
79
-------
The PRZM model simulates soil water retention in the context of
these bulk soil properties. Drainage of "excess water" is simulated as
a simple daily value or as a daily rate. Most specific model parameters
can be input directly by the user arid some can be internally estimated
given certain related soil properties as inputs.
Table 17. Pesticide Soil Application Methods and Distribution
Method of
Application
Common Procedure
Distribution
DEPI
Broadcast
Spread as dry granules
or spray over the whole
surface
Remains on the
soil surface
0.0
Disked-in
Chisel-plowed
Disking after broadcast
application
Chisel plowing after
broadcast
Assume uniform 10.0
distribution to
tillage depth
(10 cm)
Assume line;ar 15.0
distribution to
tillage depth
(1 5 cm)
Surface banded
Spread as dry granules
or a spray over a fraction
of the r ow
Remains on soil
surface
0.0
Banded incorporated Spread as dry granules
or a spray over a fraction
of the row and incorporated
in planting operation
Assume uniform
distribution to
depth of incor-
poration (5 cm)
5.0
4.4.1 Moisture Holding Capacity—(THEFC, THEWP) Card 17A
The relationship among soil properties and soil water content is
required to model the movement of water and solutes through soils.
Field capacity {THEFC) and wilting point (THEWP) are required as direct
user inputs. Often these soil-water properties have been characterized
and values can be found from soils data bases. Where such data are not
available, one of three estimation methods can be used. Method one
requires the textural properties (percent sand, silt, and clay), organic
matter content (%), and bulk density (g cm"3) of a specific soil.
Method two provides a soil triangle matrix for estimating soil water
content if only the sand (%) and clay (%) contents are known. Method
three provides mean field capacity and wilting points if only the soil
80
-------
texture is known. Eleven soil textures are provided.
Method 1 (also done within the code if THFLAG = 1)
The regression equation from Rawls, W. j. and D. L. Brakensiek.
1982. Estimating Soil Water Retention from Soil Properties. Proc.
ASCE, Vol. 108, No. IR2. June, pp 161-171, is used to estimate
the matric water potential for various soils:
Equation
9X = a + [b x SAND(%)] + (c X CLAY(%)] + [d x ORGANIC MATTER(%)] +
[e x BULK DENSITY (g crrT3) ] (41)
where 9 = water retention cm cm for a given matric
potential (field capacity = -0.33 bar and
wilting point = -15.0 bar)
a-e = regression coefficients
Procedure
Step 1. From Table 18 find the matric potential for field capacity
and wilting point (-0.33 bar and -15.0 bar).
Step 2. For each matric potential, find the regression coeffi-
cients (a-e) that are required in the Rawls and Brakensiek
equation (e.g., for -0.33 potential, coefficients a-e are
0.3486, -0.0018, 0.0039, 0.0228, and -0.0738).
Step 3. For any given soil (example: Red Bay Sandy Loam where
sand (%), 72.90; clay (%), 13.1; organic matter (%),
0.824; and bulk density (g cm"3), 1.70) solve the
equation for the -0.33 and -15.0 potential. We have
THEFC = 0.170, THEWP = 0.090.
Method 2
Use Figure 10 for estimating the field capacity and Figure 11
for estimating the wilting point of any soil, given the percent
sand and clay.
Step 1. Example: Red Bay Sandy Loam (field capacity). Find the
percent sand across the bottom of Figure 10 (i.e., 73.0).
Step 2. Find the percent clay of the soil along the side of the
triangle (i.e., 13.0).
Step 3. Locate the point where the two values intersect on the
triangle and read the field capacity, THEFC = 0.17.
81
-------
11
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82
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83
-------
Step 4.
Method 3
Follow Steps 2-4 for wilting point using Figure 11,
THEWP = 0.09.
Step 1. Use Table 19 to locate the textural class of the soil of
choice.
Step 2. After locating the textural class, read the mean field
capacity and wilting point potentials (cm3 cm"3), to the
right of the textural class.
Step 3. Example: Sandy loam. The mean field capacity (THEFC)
and wilting point (THEWP) potentials are 0.207 and 0.095,
2:espectively.
Table 18. Coefficients for Linear Regression Equations for Prediction
of Soil Water Contents at Specific Matric Potentials3
Organic Bulk
Sand Clay Matter Density
Matric Intercept (%) (%) (%) (g cm""3) R2
Coefficient a b c d e
-0.20
-0.33
-0.60
-1 .0
-2.0
-4.0
-7.0
-10.0
-15.0
0.4180 -0.0021 0.0035 0.0232 -0.0859 0.75
0.3486 -0.0018 0.0039 0.0228 -0.0738 0.78
0.2819 -0.0014 0.004-2 0.0216 -0.0612 0.78
0.2352 -0.0012 0.0043 0.0202 -0.0517 0.76
0.1837 -0.0009 0.0044 0.0181 -0.0407 0.74
0.1426 -0.0007 0.0045 0.0160 -0.0315 0.71
0.1155 -0.0005 0.0045 0.0143 -0.0253 0.69
0.1005 -0.0004 0.0044 0.0133 -0.0218 0.67
0.0854 -0.0004 0.0044 0.0122 -0.0182 0.66
aRawls, W. J., U. S. Department of Agriculture, Agricultural
Research Service, Beltsville, MD. Personal Communication.
84
-------
Table 19. Hydrologic Properties by Soil Texture3
Texture
Class
Sand
Loamy
Sand
Sandy
Loam
Range of
Textural Properties
(Percent) Water Retained at
-0.33 Bar Tension
Sand Silt Clay cm-^ cm~3
85-100 0-15 0-10 0.091b
(0.018 - 0.164)c
70-90 0-30 0-15 0.125
(0.060 - 0.190)
45-85 0-50 0-20 0.207
(0.126 - 0.288)
Water Retained at
-15.0 Bar Tension
cm^ cm" ^
0.033b
(0.007 - 0
0.055
(0.019 - 0
0.095
(0.031 - 0
.059)°
.091)
.159)
Loam
25-50 28-50 8-28
Silt Loam 0-50 50-100 0-28
Sandy Clay 45-80 0-28 20-35
Loam
Clay Loam 20-45 15-55 28-50
Silty Clay 0-20 40-73 28-40
Loam
Sandy Clay 45-65 0-20 35-55
Silty Clay 0-20 40-60 40-60
Clay
0-45 0-40 40-100
0.270
(0.195 - 0.345)
0.330
(0.258 - 0.402)
0.257
(0.186 - 0.324)
0.318
(0.250 - 0.386)
0.366
(0.304 - 0.428)
0.339
(0.245 - 0.433)
0.387
(0.332 - 0.442)
0.396
(0.326 - 0.466)
0.1 17
(0.069 - 0.165)
0.133
(0.078 - 0.188)
0.148
(0.085 - 0.211)
0.197
(0.115 - 0.279)
0.208
(0.138 - 0.278)
0.239
(0.162 - 0.316)
0.250
(0.193 - 0.307)
0.272
(0.208 - 0.336)
aRawls, W.J., D.L. Brakensiek, and K.E. Saxton. Estimation of Soil
Water Properties. Transactions ASAE Paper No. 81-2510, pgs. 1316 - 1320.
1982.
bMean value.
cOne standard deviation about the mean.
85
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4.4.2 Bulk Density and Field Saturation—(BD) Card 17
Soil bulk density (BD) is required in the basic chemical transport
equations of PRZM and is also used to estimate moisture saturation
values. Values for BD are input directly, when such data are not
available for the site of interest, methods have been developed for
their estimation. Two methods are provided for estimating BD of
various soils. Method one requires the textural properties (percent
sand, clay, and organic matter). Method two uses mean bulk density
values if only the soil texture is known. The following "steps provide
procedures for estimating bulk density.
Method 1 (Also done within the code if BDFLAG = 1)
A procedure from Rawls, W. 0. 1983. Estimating Soil Bulk Density.
Soil Science. 135(2). pp 123-125, is used to estimate bulk density
for any given soil, provided the percent sand, clay, and organic matter
contents are known. Example: Marlboro fine sandy loam--sand 80.0%,
clay 5.0%, and organic matter 0.871%.
Equation
BD = 100.0 (42)
%OM + 100.0 - %OM
OMBD MBD
where BD = soil bulk density, g cm~3
OM = organic matter content of soil, %
OMBD = organic matter bulk density of soil, g cm~3 = 0.224
MBD = mineral bulk density, g cm~^
NOTE: MBD must be entered on CARD 17 if BDFLAG = 1
Step 1. Locate the percent sand (80.0) along the bottom of
Figure 12.
Step 2. Locate the percent clay (5.0) along the side of
Figure 12.
Step 3. Locate the intersect point of the two values and
read the mineral bulk density (1.55).
Step 4. Solve the Rawls equation for BD (e.g., 1.47). If
BDFLAG = 1, the mineral bulk density is entered
on Card 17 (e.g., 1.55).
86
-------
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0)
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• en
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87
-------
Method 2
Step 1. Use Table 20 to locate the textural classification of the
soil.
Step 2. Read mean bulk density for the general soil texture.
Step 3. Elxample: Sandy loam. The mean bulk density is 1 .49
g cm"3.
Table 20. Mean Bulk Density (g cm"3) for Five Soil
Textural Classifications3
Soil Texture
Silt Loams
Clay and Clay Loams
Sandy Loams
Gravelly Silt Loams
Loams
All Soils
Mean
1 .
1 .
1 .
1 .
1 .
1 .
Value
32
30
49
22
42
35
Range
0
0
1
1
1
0
.86
.94
.25
.02
.16
.86
Reported
- 1
- 1
— 1
- 1
- 1
- 1
.67
.54
.76
.58
.58
.76
aBaes, C. F., Ill and R. D. Sharp. 1983. A Proposal for
Estimation of Soil Leaching Constants for Use in Assessment
Models. J. Environ. Qual. 12(1): 17-28.
4.4.3 Soil Moisture Estimation Technique Problems
PRZM currently is structured to permit ease of operation in provid-
ing for direct estimation of input variables for water movement includ-
ing field capacity, wilting point, bulk density, and field saturation.
In certain poorly drained soils (with clay contents in the upper bound
of the classification), it is possible that the calculated field capaci-
ty may exceed the calculated field saturation values. PRZM will identi-
fy such instances. Two options are available if such an error is encoun-
tered. The first option is a simple correction by assuming that the
saturation value, THETAS, is a constant value in excess of the field
capacity. This correction is described by
THEfAS = THEFC + 0.122 (43)
-------
The second approach is simply to estimate a corrected value from Table
21 .
Should the inconsistency between field capacity and saturation
values occur, it will be necessary to make the corrections by adjusting
THEFC or BD. Editing the FORTRAN code is another alternative.
4.4.4 Options for Estimating Soil Water Drainage—(HSWZT) Card 15
The HSWZT flag indicates which drainage model is invoked for
simulating the movement of recharging water. Drainage model 1 (HSWZT =
0) is for freely draining soils; drainage model 2 (HSWZT = 1) is for
more poorly drained soils. For soils with infiltration rates of more
than 0.38 cm hr~1 (associated with SCS hydrologic soils groups A, B,
and some C), setting HSWZT = (3 is recommended. For soils with infiltra-
tion rates of less than 0.38 cm hr~^ (associated with groups D and
some C) setting HSWZT = 1 is recommended.
4.4.5 Soil Water Drainage Rate (for HSWZT = 1)—(AD) Card 17
The drainage rate parameter (AD) used in the HYDR2 hydraulics op-
tion of PRZM is an empirical constant and dependent on both soil type
and the number of compartments to be simulated. Although there is
limited experience using this option, an analysis was performed to
determine the best value for AD over a range of soil types on which
agricultural crops are commonly grown. Each of three soil types was
tested with a constant soil profile depth (125 cm). The profile was
divided into a variable number of compartments and the optimum value of
AD for each soil/compartment combination was obtained.
The analysis was performed by comparing the storage of water in
the soil profile following the infiltration output from SUMATRA-1 (53).
This model was used as "truth" because field data were lacking and
SUMATRA-1 is theoretically rigorous. The amount of water moving out of
the profile changed by only 1 - 2% over the range of compartments tested
(15 - 40) for the three soils evaluated. Calibrating PRZM by comparison
was accomplished and estimates of AD calculated. Suggested values of AD
for clay loam, loamy sand, and sand as a function of the number of com-
partments are given in Figure 13. This relationship and guidance will
be updated as additional experience is gained in its use.
89
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Table 21 . Hydrologic Properties by Soil Texturea
Texture
Class
Sand
Loamy Sand
Sandy Loam
Loam
Silt Loam
Sandy Clay Loam
Clay Loam
Silty Clay Loam
Sandy Clay
Silty Clay
Clay
Residual
Porosity
(er)
0.020b
(0. 001-0. 039)c
0.035
(0.003-0.067)
0.041
(0.0-0.106)
0.027
(0.0-0.074)
0.015
(0.0-0.058)
0.068
(0.0-0.237)
0.075
(0.0-0.174)
0.040
(0.0-0.1 18)
0.109
(0.0-0.205)
0.056
(0.0-0.136)
0.090
(0.0-0.195)
Effective
Porosity
(ee)
cm3 cm"3
0.417
(0.354-0.480)
0.401
(0.329-0.473)
0.412
(0.283-0.541 )
0.434
(0.334-0.534)
0.486
(0.394-0.578)
0.330
(0.235-0.425)
0.390
(0.279-0.501 )
0.432
(0.347-0.517)
0.321
(0.207-0.435)
0.423
(0.334-0.512)
0.385
(0.269-0.501 )
aRawls, W.J., D.L. Brakensiek, and K.E. Saxton. Estimation of Soil
Water Properties. Transactions ASAE Paper No. 81-2510, pgs. 1316 - 1320.
1982.
^Mean value.
GOne standard deviation about the mean.
90
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2.8-
2.4-
2.0-
c
1.6-
1.2-
15
Figure 13.
20 25 30 35
Number of compartments
Sand
Loamy Sand
Clay Loam
40
Estimation of drainage rate AD (day -1-) versus number
of compartments.
91
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SECTION 5
OPERATIONAL MODELING CONSIDERATIONS
5.1 INTRODUCTION
The primary purpose of this guide is to assist users in applying
PRZM in evaluations of potential groundwater contamination from pesti-
cide use. Considerable effort was directed towards estimation techni-
ques for many of PRZM's data requirements with the ultimate goal of
efficient model utilization. In this chapter, several general model-
ing considerations that the user should be aware of are described.
A decription of how to obtain the PRZM code and installation on
a DEC POP 11/70 mini computer is also provided.
5.2 AQUISITION PROCEDURES
To obtain the PRZM program along with a sample data set and/or
supporting data, write to:
Technology Development & Applications Branch
Attention: PRZM Code Request
Environmental Research Laboratory
U.S. Environmental Protection Agency
College Station Road
Athens, Georgia 30613
A nine-track tape will be mailed to you. The program is designed
for a DEC PDP mini computer. Modifications may be required for
operation on other machines.
5.3 INSTALLATION PROCEDURES
Among the data sets on the magnetic tape are the subroutines of
the modularized PRZM code. These must be compliled and linked into
a task image. This is accomplished on the IAS operating system by
running the command file "PRZM.BIS," which is listed below:
92
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$JOB EPARFE PRZM 999
$!
$! PRZM.BIS DB2:[205,221] PRZM.BIS
$!
$ON WARNING CONTINUE
$DELETE PRZM.TSK;*
$DELETE PRZM.MAP;*
$DELETE/KEEP PRZM.*
$!
$!
$FORTRAN PRZM
$FORTRAN BLOCKPRZM
$FORTRAN ECHO
$FORTRAN EROSN
$FORTRAN EVPOTR
$FORTRAN FTIME ! SPECIAL SUBROUTINE ADDED
$FORTRAN HYDR1
$FORTRAN HYDR2
$FORTRAN HYDROL
$FORTRAN INITL
$FORTRAN KDCALC
$FORTRAN MASBAL
$FORTRAN OUTCNC
$FORTRAN OUTHYD
$FORTRAN OUTPST
$FORTRAN OUTTSR
$FORTRAN PESTAP
$FORTRAN PLGROW
$FORTRAN PLPEST
$FORTRAN READ
$FORTRAN SLPEST
$FORTRAN THCALC
$FORTRAN TRDIAG
$!
$!
$ON WARNING GOTO NEXT
$LINK/OPTION/READ/TASK:PRZM/MAP:(PRZM/FULL)/OVERLAY:PRZM
ACTFIL=6
UNITS=10
/
$!
$!
$DELETE PRZM.OBJ;*
$DELETE BLOCKPRZM.OBJ;*
$DELETE ECHO.OBJ;*
$DELETE EROSN.OBJ;*
$DELETE EVPOTR.OBJ;*
$DELETE FTIME.OBJ;*
$DELETE HYDR1.0BJ;*
$DELETE HYDR2.0BJ;*
$DELETE HYDROL.OBJ;*
93
-------
$DELETE INITL.OBJ;*
$DELETE KDCALC.OBJ;*
$DELETE MASBAL.OBJ;*
$DELETE OUTCNC.OBJ;*
$DELETE OUTHYD.OBJ;*
$DELETE OUTPST.OBJ;*
$DELETE OUTTSR.OBJ;*
$DELETE PESTAP.OBJ;*
$DELETE PLGROW.OBJ;*
$DELETE PLPEST.OBJ;*
$DELETE READ.OBJ;*
$DELETE SLPEST.OBJ;*
$DELETE THCALC.OBJ;*
$DELETE TRDIAG.OBJ;*
$!
$!
$NEXT: SHOW TIME
$DELETE/KEEP PRZM.*
$DELETE/KEEP BLOCKPRZM.*
$DELETE/KEEP ECHO.*
$DELETE/KEEP EROSN.*
$DELETE/KEEP EVPOTR.*
$DELETE/KEEP FTIME.*
$DELETE/KEEP HYDR1.*
$DELETE/KEEP HYDR2.*
$DELETE/KEEP HYDROL.*
$DELETE/KEEP INITL.*
$DELETE/KEEP KDCALC.*
$DELETE/KEEP MASBAL.*
$DELETE/KEEP OUTCNC.*
$DELETE/KEEP OUTHYD.*
$DELETE/KEEP OUTPST.*
$DELETE/KEEP OUTTSR.*
$DELETE/KEEP PESTAF1.*
$DELETE/KEEP PLGROW.*
$DELETE/KEEP PLPEST.*
$DELETE/KEEP READ.*
$DELETE/KEEP SLPEST.*
$DELETE/KEEP THC'ALC.*
$DELETE/KEEP TRDIAG.*
$!
$!
$DIRECTORY/FULL PRZM.*;*
$EOJ
5.4 TESTING PROCEDURES
Once PRZM is installed, the sample input data set should be run
and compared with the sample output data set to verify that the program
94
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is calculating correctly. An example data set describing an agricul-
tural area in peanut production is provided on the tape (and described
in Section 6).
Simulations are run in a batch mode (unless the ANPRZM pre-
processor, which is detailed in this section, also is used). To
perform a simulation on the PDF, submit the batch input sequence
"RUNPRZM," which is listed below:
$JOB EPARFE RUNPRZM 9999
$!
$! This batch file allows you to make a PRZM run by assigning
$! devices to the proper units. This file also allows you to save
$! or delete old output files.
$!
$ON WARNING CONTINUE
$!
$! Delete old data files. (Comment out deletes if old files should
$! remain)
$DELETE DB1:PRZCN.DAT;*
$DELETE DB1:PRZPS.DAT;*
$DELETE DB1:PRZTS.DAT;*
$DELETE DB1 :PRZWT.DAT; *
$!
$! Assign units to devices. (Strictly for our 11/70 system set up)
$!
$ASSIGN DB2
$ASSIGN DB2
$ASSIGN DB1
$ASSIGN DB1
$ASSIGN DB1
$ASSIGN DB1
2
3
4
7
8
9
$!
$! Run the PRZM program.
$!
$SHOW TIME
$RUN PRZM
$SHOW TIME
$!
$! Deassign units to devices. (Strictly for our 11/70 system set
$! up)
$DEASSIGN 2
$DEASSIGN 3
$DEASSIGN 4
$DEASSIGN 7
$DEASSIGN 8
$DEASSIGN 9
$!
$EOJ
95
-------
5.5 MACHINE LIMITATIONS
Currently, PRZM is set up for the following configurations.
POP 11/70 Hardware
IAS Operating System
FORTRAN IV
25 parameters
85 constants
50 segments
2 systems
The POP 11/70 computer utilizing an IAS operating system allo-
cates a 32k word (64 byte) user area for execution of programs. PRZM
occupies 45K words of memory. An overlay routine effectively reduces
the storage memory to 31K.
A compromise in the size of the program arrays (regulated by
the PARAMETER statements at the beginning of the common area) will
result in more free area for other applications. Changes to the
code should be completely researched and tested.
5.6 ANPRZM: A PRE-PROCESSING MODULE FOR INTERACTIVE MODELING
ANPRZM is a FORTRAN program designed to provide interactive
capability for PRZM to create, check, and update input streams? of
data. ANPRZM was developed to reduce the time and effort required in
setting up hydrologic models for calibration/verification/production
analyses. ANPRZM also was designed to be helpful to the inexperienced
user and yet be efficient for the experienced user. PRZM has a
distinct category of input: watershed/pesticide characteristics
such as field capacity, wilting point, curve number, crop type,
partition coefficient, and decay rate.
For the category of input, ANPRZM provides easy creation of the
files by prompting the user for parameters, checking the parameters
for agreement to acceptable ranges, and providing default values.
The user may decide to change an option within PRZM, which can affect
other input required. ANPRZM will provide checks for those type
situations, thus saving time from submitting jobs that fail due to
missing data. Experience has indicated these types of errors are
major sources for extending simulation effort (and frustration).
The code is written in ANSI FORTRAN with coding conventions
established and concepts of structured programming used in develop-
ment.
96
-------
The code contains utility subroutines and control/logic subrou-
tines. Tine inputs are by line (provides terminal compatibility).
All terminal inputs and outputs are placed in one or small groups of
subroutines in order to provide conversion to full screen interaction
if desired. The questions are placed in direct access files and are
less than 60 characters. Inputs are read as character data with one
character per word to provide ease of manipulation by the utility
subroutines. Alphanumeric responses use only sufficient characters
to distinguish one option from others. Examples are done as "d" or
yes as "ye".
For interactive processing, ANPRZM uses a series of questions and
answers following a menu in which the response determines the next
question for display. The response for any part of the menu may be
a series of questions and responses, or, a line format may appear on
the screen with entries (changes) at locations beneath the line
entered. If ANPRZM is used to update a current simulation run, only
selected values to be changed are entered.
When an ANPRZM session is over, the user may go back to another
level of the menu. To move to another level the input block (e.g.,
Soil) is entered.
ANPRZM is best suited for setting up files, checking data input
for appropriateness, and for instruction/demonstration using PRZM.
If only a single value (such as the curve number for the cropping
period) is to be changed, a text editor is more appropriate.
The user is responsible for evaluating whether the model is
appropriate for the intended use, the types of data required, the
basic components of the model (including files and formats), and
what analyses are to be accomplished with the time-series data
generated. ANPRZM is not intended to provide artificial intelli-
gence—the cliche "garbage" in "garbage" out still applies.
To complete a simulation using a dynamic hydrologic model such as
PRZM, three efforts are required: (1) the input stream for the model
must be developed, (2) the climatic data must be placed on a file in
the required model format, and (3) time-series output has to be ana-
lyzed. ANPRZM reduces the time to accomplish item one of the three
efforts required of a simulation with PRZM.
To compile and link the ANPRZM software module, the command file
ANPRZM.BIS is run on the IAS operating system, which is listed below.
$JOB EPARFE ANPRZM 9999
$!
$! ANPRZM.BIS DBO: [205,221] ANPRZM.BIS
$!
$ON WARNING CONTINUE
$DELETE ANPRZM.TSK;*
97
-------
$DELETE ANPRZM.MAP;*
$!
$FORTRAN ANPRZM
$FORTRAN BLKANPRZM
$FORTRAN CHKINT
$FORTRAN CHKREA
$FORTRAN CHKSTR
$FORTRAN CHRCHR
$FORTRAN CHRDEC
$FORTRAN CHRDIG
$FORTRAN CHRINT
$FORTRAN DATCHK
$FORTRAN DECCHR
$FORTRAN DIGCHR
$FORTRAN FILCHK
$FORTRAN GETTXT
$FORTRAN INTCHR
$FORTRAN JDAY
$FORTRAN JDYDYM
$FORTRAN LENSTR
$FORTRAN MODCRP
$FORTRAN MODHYD
$FORTRAN MODOUT
$FORTRAN MODPST
$FORTRAN MODSOI
$FORTRAN PRNTXT
$FORTRAN PRZFOU
$FORTRAN PRZTIN
$FORTRAN QFLOUT
$FORTRAN QREC
$FORTRAN QRESP
$FORTRAN QRESPM
$FORTRAN READ
$FORTRAN REAOLD
$FORTRAN WRITE
$!
$LINK/OPTION/READ/TASK:ANPRZM/MAP:(ANPRZM/FULL)/OVERLAY:ANPRZM
ACTFIL=4
UNITS=1 0
/
$!
$DELETE ANPRZM.OBJ;*
$DELETE BLKANPRZM.OBJ;*
$DELETE CHKINT.OBJ;*
$DELETE CHKREA.OBJ;*
$DELETE CHKSTR.OBJ;*
$DELETE CHRCHR.OBJ;*
$DELETE CHRDEC.OBJ;*
$DELETE CHRDIG.OBJ;*
$DELETE CHRINT.OBJ;*
$DELETE DATCHK.OBJ;*
98
-------
$DELETE DECCHR.OBJ;*
$DELETE DIGCHR.OBJ;*
$DELETE FILCHK.OBJ;*
$DELETE GETTXT.OBJ;*
$DELETE INTCHR.OBJ;*
$DELETE JDAY.OBJ;*
$DELETE JDYDYM.OBJ;*
$DELETE LENSTR.OBJ;*
$DELETE MODCRP.OBJ;*
$DELETE MODHYD.OBJ;*
$DELETE MODOUT.OBJ;*
$DELETE MODPST.OBJ;*
$DELETE MODSOI.OBJ;*
$DELETE PRNTXT.OBJ;*
$DELETE PRZFOU.OBJ;*
$DELETE PRZTIN.OBJ;*
$DELETE QFLOUT.OBJ;*
$DELETE QREC.OBJ;*
$DELETE QRESP.OBJ;*
$DELETE QRESPM.OBJ;*
$DELETE READ.OBJ;*
$DELETE REAOLD.OBJ;*
$DELETE WRITE.OBJ;*
$!
$DELETE/KEEP ANPRZM.*
$DELETE/KEEP BLKANPRZM.*
$DELETE/KEEP CHKINT.*
$DELETE/KEEP CHKREA.*
$DELETE/KEEP CHKSTR.*
$DELETE/KEEP CHRCHR.*
$DELETE/KEEP CHRDEC.*
$DELETE/KEEP CHRDIG.*
$DELETE/KEEP CHRINT.*
$DELETE/KEEP DATCHK.*
$DELETE/KEEP DECCHR.*
$DELETE/KEEP DIGCHR.*
$DELETE/KEEP FILCHK.*
$DELETE/KEEP GETTXT.*
$DELETE/KEEP INTCHR.*
$DELETE/KEEP JDAY.*
$DELETE/KEEP JDYDYM.*
$DELETE/KEEP LENSTR.*
$DELETE/KEEP MODCRP.*
$DELETE/KEEP MODHYD.*
$DELETE/KEEP MODOUT.*
$DELETE/KEEP MODPST.*
$DELETE/KEEP MODSOI.*
$DELETE/KEEP PRNTXT.*
$DELETE/KEEP PRZFOU.*
$DELETE/KEEP PRZTIN.*
$DELETE/KEEP QFLOUT.*
99
-------
$DELETE/KEEP QREC.*
$DELETE/KEEP QRESP.*
$DELETE/KEEP QRESPM.*
$DELETE/KEEP READ.*
$DELETE/KEEP REAOLD.*
$DELETE/KEEP WRITE.*
SI
$DIRECTORY/FULL ANPRZM.*;*
$!
$SRD /SN/LI/FU
$SRD /ST/LI/FU
$!
$EOJ
PRZM-compatible ANPRZM is provided with the PRZM distribution
tape. Detailed documentation of the code is found in ANNIE - An
Interactive Processor for Hydrologic Modeling by Alan M. Lumb
and John L. Kittle, Jr., U.S. Geological Survey, Water-Resources
Investigations Report. (USGS documentation number not yet assigned)
5.7 USE OF PRZM FOR IRRIGATED AGRICULTURE
PRZM is designed primarily to evaluate pesticide leaching in
areas where rainfall is the source of water. It is possible, however,
to use the model to evaluate leaching under irrigated systems if
special attention is given to the water balance components of the
model. In short, if the water balance, including percolation and
recharge, is computed properly, the chemical transport or pesticide
leaching simulated by PRZM should provide useful results.
In the western United States irrigated crops are a major part
of the agriculture water management budget. Water applied as
irrigation may evaporate from the soil or crop surface, run off, or
leave as leachate. Water applied in excess of crop demand may per-
colate below the root zone of the crop and carry soluble pesticides
with the percolating water. Many irrigated areas may have geologic
and soil characteristics that may favor water losses (from excess
irrigation) through leachate.
The irrigation requirement is determined from crop ET demand
and effective rainfall (water left after runoff and soil storage).
Sections 2 and 4 have provided runoff and soil storage requirements
for various soils. The average ET demand for various crops in
irrigated areas is required for simulation. These data, provided in
Table 22, are useful for describing the correct hydrologic response
in arid or semi-arid agricultural settings. That is, calibration of
the hydrologic component of the model to these values with precipita-
100
-------
Table 22. Selected Examples of Observed Seasonal Evapotranspiration
for Well-Watered, Common Crops in the U.S.A.a
Crops
Forage Crops
Alfalfa
Alfalfa
Clover, ladino
Alfalfa
Alfalfa
Alfalfa
Alfalfa
Grass
Grass
Grass
Grass
Grass
Grain and Field
Barley
Barley
Barley
Beans
Beans
Beans
Corn
Corn
Corn
Corn
Corn
Corn
Corn
Potatoes
Potatoes
Potatoes
Rice
Sorghum
Sorghum
Sorghum
Annual Average
Evapotranspiration
Location (cm)
Upham , N . D .
Mitchell, Nebr.
Prosser, Wash.
Kimberly, Ida.
Reno, Nev.
Arvin, Calif.
Mesa and Tempe, Ariz.
Davis, Calif. (Sacramento Valley)
Arvin, Calif. (San Joaquin Valley)
Thornton, Calif. (Delta)
Soledad, Calif. (Salinas Valley)
Guadalupe, Calif. (Coastal)
Crops
Powell, Wyo.
Mesa, Ariz.
Davis, Calif.
Powell, Wyo.
Redfield, S. Dak.
Davis, Calif.
Upham , N . Dak .
Redfield, S. Dak.
Powe 1 1 , Wyo .
Coshocton, Ohio
Hot Springs, S. Dak.
Bushland, Tex.
Davis, Calif.
Upham, N. Dak.
Mandan, N. Dak.
Phoenix, Ariz.
Davis, Calif.
Garden City, Kans.
Bushland, Tex.
Me s a , Ar i z .
59.4
74.7
85.9
91 .6
101 .3
127.5
188.7
131 .6
130.8
1 19.6
123.2
100.6
38.6
64.3
38.4
39.6
41 .7
40.4
44.5
42.2
41 .4
47.0
53.6
61 .7
64.0
46.7
45.5
61 .7
92.0
55.1
54.9
64.5
101
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Table 22. Selected Examples of Observed Seasonal Evapotranspiration
for Well-Watered, Common Grain and Field Crops in the U.S.A.
(Continued)
Crops
Wheat
Wheat
Wheat, Mexican
Wheat, winter
Sugar Crops
Sugarbeet
Sugarbeet
Sugarbeet
Sugarbeet
Sugarbeet
Sugarbeet
Sugarbeet
Oil Crops
Castorbean
Saf flower
Saf flower
Soybean
Soybean
Fiber Crops
Cotton
Cotton
Flax
Flax
Vegetable Crops
Broccoli
Cabbage, early
Cabbage, late
Location
Redfield, S. Dak.
Mesa, Ariz .
Mesa, Ariz.
Bushland, Tex.
Huntley, Mont.
Redfield, S. Dak.
Kimberly, Ida.
Davis, Calif.
Garden City, Kans.
Bushland, Tex.
Mesa, Ariz.
Mesa, Ariz.
Mesa, Ariz.
Kimberly, Idaho
Redfield, S. Dak.
Mesa, Ariz.
Arvin, Calif.
Mesa and Tempe, Ariz.
Redfield, S. Dak.
Mesa, Ariz.
Mesa, Ariz.
Mesa, Ariz .
Mesa, Ariz.
Annual Average
Evapo transpiration
(cm)
41 .4
58.2
65.5
71 .9
57.2
61 .0
61 .7
85.1
92.7
99.1
105.4
112.8
115.3
63.5
39.9
56.4
91 .2
104.6
38.1
79.5
50.0
43.7
62.2
Cantaloupe
Mesa, Ariz.
48.5
102
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Table 22. Selected Examples of Observed Seasonal Evapotranspiration
for Well-Watered, Common Crops in the U.S.A. (Continued)
Annual Average
Evapotranspiration
Crops Location (cm)
Vegetable Crops
Carrots
Cauliflower
Corn, sweet
Lettuce
Onion, dry
Onion, green
Tomato
(Continued)
Mesa,
Mesa,
Mesa,
Mesa,
Mesa,
Mesa,
Davis
Ariz .
Ariz .
Ariz .
Ariz .
Ariz .
Ariz.
, Calif.
42.2
47.2
49.8
21 .6
59.2
44.5
68.1
ajensen, M. E. (Ed.). Consumptive Use of Water and Irrigation
Requirements. Amer. Soc. Civil Engrs., New York, NY. 1982.
tion inputs changed to irrigation inputs may enable further estimates
of leaching. These data should not be used for evapotranspiration
demands where rainfall exceeds crop water demands. Bruce et al.
(Irrigation of Crops in the Southeastern United States Principles
and Practice. USDA publication ARM-S-9/May 1980) provide procedures
for plant and soil-water principles to irrigated crops in the South-
eastern United States.
5.8 AUXILIARY INFORMATION
The major factors that will determine the success and accuracy
of specific site or regional simulations are availability of soils/
geologic data and climatological information. Without knowledge of
surface/subsurface characteristics (such as water holding capacities)
or suitable information from which to estimate such properties,
evaluations may be largely conjecture. To establish the correct
hydrologic response for site specific or regional simulations, con-
103
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siderable data may be required. These data are not generally col-
lected and aggregated in one central location. The user may spend
considerable effort (see Section 6) on developing the input data for
model simulations.
In recognition of these limitations several supportive data bases
are available with PRZM. The first data base is meteorological infor-
mation. A search was conducted of reporting weather stations that
provide daily.precipitation, pan evaporation, and temperature (required
PRZM meteorological information) data. A total of approximately 300
stations (having at least 25 years of records) were identified within
the continental United States. These records have been assembled onto
a disk and an interactive retrival program has been developed to
retrieve and assemble the data into PRZM format for use. The second
data base consists of generalized soils information assembled from the
Soil Conservation Service (Soils Series Investigation Reports) and
tabularized onto disk for computer retrival. In addition, the Soil
Conservation Service currently has a detailed soils interpretation
data base that provides soil/cropping information for some 20,000 soil
series. This data base is constantly being updated and is the soils
data base recommended for use with PRZM. EPA's Environmental Research
Laboratory, Athens, GA, currently provides technology for developing
exposure assessment techniques. Several PRZM model application pro-
jects have been conducted and one such effort has generated a catalog
of 19 major agricultural use areas in the United States with PRZM
formatted data input.
This guide is not intended to be a totally stand-alone document
for assessing potential ground water contamination--the supporting
information required would occupy a guide many times the size of this
report. The information/data bases presented here are intended to
augment the utility and flexibility of. PRZM as well as increase its
efficiency to the user.
104
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SECTION 6
SIMULATION STRATEGY
6.1 INTRODUCTION
PRZM application to a specific problem requires development of
a simulation plan or strategy. The site must be characterized with
regard to meteorologic conditions, soil qualities, and land management
practices. Characterization of soils and land management conditions
must be developed to define the hydrologic response of the area;
pesticide properties determine the behaviour of the chemical. The
successful development and implementation of a simulation plan
requires completion of five tasks that require varying degrees of
effort.
The five tasks and the nominal relative effort required for
their completion are: (1) definition of problem, 5%; (2) primary
intent description and operational learning curve, 20%; (3) develop-
ment and input of data set, 40%; (4) calibration and sensitivity
analysis, 20%; and documentation and reporting of results, 15%. The
degree of effort listed for each specific task is a general indicator
only, and each task may vary depending on the specific problem, user
experience, and availability of data.
Hydrologic components are similar for an area, but may vary con-
siderably for individual sites within that area. The majority of the
PRZM-required hydrologic components have not been deterministically
evaluated for individual sites and calibration may be required.
The many climatologic, hydrologic, agronomic, and pesticide
characteristics create numerous and diverse scenarios that may have
to be investigated when simulating pesticide leaching potential.
The use of sensitivity analysis can, however, reduce the number of
simulations substantially.
The major emphasis of this section is a discussion of the five
steps of a simulation strategy coupled with a demonstration of an
example problem, with associated calibration procedures and sensiti-
vity analyses. The guidance presented is not intended to provide the
user with a detailed discussion of modeling practices.
105
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6.2 DEFINITION OF EXAMPLE PROBLEM
A systemic pesticide has been found to be effective against a
root disease in peanuts. The disease is predominant in the young
emerging plant and application has to be made at the time of planting
or shortly afterward (up to one month after planting). The expected
use of the chemical will be immediate and extensive. The chemical
has a water solubility of (800 mg/1 at 20°C), possesses a decay rate
of 0.0134 days'"^ , and has a distribution coefficient of 0.8 1/mg for
soils with 0.50% organic carbon. Concern has been expressed regarding
its leaching potential and possible groundwater contamination. A
detailed exposure assessment of the likelihood that it would reach
groundwater is required.
6.3 PRIMARY INTENT DESCRIPTION/OPERATIONAL LEARNING CURVE
The second step in developing a simulation strategy (after
defining the problem) is to develop the primary intent for the
evaluation. The primary intent may just be a rapid screening assess-
ment that would involve minimal data gathering and very few simula-
tions. If the intent is to provide frequency durations and probabili-
ty curves, however, the primary intent will encompass a detailed
effort involving intensive data gathering and several simulations.
The primary intent process can be divided into three categories:
1. Identify components that must be addressed with the model
application and determine the level of detail required to
analyze the components identified.
2. Review available (supporting) data and their appropriate-
ness to the modeling components identified.
3. Estimate the time and resources that are required for
the assessment.
The quality of modeling results reflect the quality of the data
used to apply the model. If the data used to characterize the area
are accurate and comprehensive, a higher degree of confidence in
model representation of the study area is obtained. A good comparison
between simulated and observed values (if available) indicates the
model is adequately representing the critical processess in the
study area.
PRZM is a new model, with several applications either in progress
or completed; consequently, information on resources associated with
model application is limited to a few pilot studies. The model does
include a fairly comprehensive data base, and time and resources
required for an evaluation are minimized.
106
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6.4 DEVELOPMENT OF INPUT DATA SET
For the purposes of PRZM, the simulated hectare parcel of land
represents a homogeneous hydrologic response, that is, all of the
land in the simulation would exhibit similar properties.
The example crop provided is peanuts. The first step would be
to locate the major growing areas for peanuts. Agricultural statis-
tics from the U.S. Department of Agriculture (USDA) show that Florida,
Alabama, and Georgia produce most of the nation's peanuts. Georgia
produces 61% of the acreage alone. For demonstration of leaching
potential, Georgia is chosen for an example location. The USDA
Statistical Reporting Service and the Georgia Crop Reporting Service
was consulted to identify major peanut growing areas in Georgia.
The Dougherty Plain area is an indicated heavy production area.
The topography of the Dougherty Plain is characterized by rela-
tively level or gently undulating land area where altitudes range
from 210 to 222 feet. Approximately 80% of the area is used for
agriculture. Soils were formed from four geologic sources—the Ocala
Limestone, the Flint River, and the McBean and Wilcox formations—all
of which were deposited in the tertiary age followed by more recent
alluvium deposits. A large peanut growing area is in the southwest
section where extensive formations of the Ocala Limestone and Flint
River occur with typical soils falling into Tifton, Greenville,
Orangeburg, Red Bay, Grady, Faceville, Marlboro, and Norfolk series
(SCS Athens, GA. 1983).
The area is characterized by a warm, humid climate with long,
hot summers and short mild winters. Rainfall averages 127 cm a year
with evapotranspiration running 60 to 80 cm per year. The soils are
mostly level to gently sloping (0 - 2% slopes) with the water table
several feet below the surface. Flooding does not occur and drainage
is good (SCS, Athens, GA. 1983). Runoff potential is low (15-20 cm
per year). For demonstration, the widespread Norfolk sandy loam will
be used.
Typical soil profiles for Norfolk sandy loam are provided in
Table 23.
Peanuts are usually planted from April to May and harvested in
mid October to early November. The crop is shallow rooted with 30 to
60 cm typical of the crop. Modified tillage consists of spring mold-
board plowing (15 cm), disking, planting, and not cultivating.
Dates of tillage and application of pesticide are provided in
Table 24.
A card sequence for the example problem is developed. For some
of the parameters where interpretation or further elaboration is
required, the logic used in estimating the parameter is provided.
107
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Table 23. Soil Properties for Norfolk Sandy Loama
Moisture
Depth
(cm)
0 -
17 -
30 -
40 -
58 -
76 -
100 -
17
30
40
58
60
100
135
Potential
Field Wilting
Textural Properties Capa- Point pH
Percent city
Sand Silt Clay OC cm3 cm3
86.0
75.0
65.4
68.7
71 .5
69.0
58.5
10.0
14.2
1 4.8
12.2
1 1 .3
10.5
12.0
4.0
10.8
19.8
19.1
18.2
20.5
29.5
0.54
0.22
0.25
0.10
0.1 1
0.04
0.04
11 .0
1 0.9
14.9
14.2
14.3
15.0
17.0
1
3
5
9
8
9
12
.2
.6
.8
.4
.8
.8
.5
6
5
4
5
5
5
4
.3
.5
.8
.3
.4
.0
.8
Bulk Hydro-
Density logic
g cm3 Class Drainage
1 .68 B GOOD
1 .76
1 .68
1 .62
1 .58
1 .73
1 .74
amean of several reported series from SCS
Table 24. Tillage Operations for Continuous Peanuts
Date
Each
Year
March
March
April
April
Nov.
1
28
1
10
1
Field Pesticide Crop Yield Crop Factor
Operation3 Rate (Ibs A~1 )
(kg ha~1 )
Moldboard Plow
Disk
Plant/Apply
Pesticide 2.0
Plant Emergence
Harvest Crop 2550 1 .5
aAssumes modified tillage with continuous peanuts.
108
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CARD SEQUENCE
CARD COLUMN
1 23456789m 23456789^1 2345678931 234567894,1 234567895J 2345678961 23456789^71 2345
67898_
t CARD 1.0: TITLE (FORMAT 20A4) ]
SIMULATION PEANUTS - GEORGIA
[ CARD 2.0: BEGINNING AND ENDING DATE(S) OF SIMULATION
(FORMAT 2x, 312, 10x, 312) ]
010150 311277
[ CARD 3.0: HTITLE (FORMAT 20A4) ]
HYDROLOGY PARAMTERS
[ CARD 4.0: PAN AND SNOW FACTORS, PAN FLAG, EVAPORATION DEPTH, INITIAL
CROP, SURFACE CONDITION INITIAL CROP AFTER HARVEST (FORMAT 2F8.0, 18,
F8.0, 218) ]
0.75 0.457 0 25.0 1 3
CARD 4: Pan factor, snow factor, depth of soil evaporation, initial crop
and surface condition
The pan factor (PFAC) is used to convert daily pan evaporation
into daily potential evapotranspiration. The Dougherty Plain is
located in the southwestern corner of Georgia. From Figure 4 the
isopleth transecting this section of Georgia is 75. PFAC is reported
as a percentage (required in fractional form in the model) and 0.75
is entered for PFAC.
The snow factor (SFAC) is used in the snow algorithm for
estimating the amount of snow accumulated or melted. The National
Oceanic and Atmospheric Administration provides climatic records for
the United States. The Dougherty Plain averages less than 2.0 cm
snow/year accumulation. Snow accumulation will not be a predominant
part of the water budget. The mean value of 0.457 provided from
Section 4 is entered for SFAC.
The amount of water evaporated from the soil surface is governed
by a user specified depth (ANETD). From Figure 5, a range of 20-30
cm is provided and a mean of 25.0 cm is estimated and entered for
ANETD.
The initial crop (INICRP) and surface condition (ISCOND) are
required if the first day of simulation is before the first day of
109
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crop emergence (a condition that is met in the Dougherty Plain
example). Because only one crop is simulated, INICRP = 1. The sur-
face condition after harvest is either cropping, fallow, or residue
(with corresponding values of 1, 2, or 3); for the Dougherty Plain
the residue condition exists and 3 is entered for ISCOND.
[ CARD 5.0: EROSION FLAG (FORMAT 18) ]
0
CARD 5: Erosion flag (ERFLAG)
The erosion flag is set equal to zero because the partition
coefficient is less than 5.0 as suggested in Section 4.
[ CARD 6.0: NUMBER OF DIFFERENT CROPS (FORMAT 18) ]
[ CARD 7.0: CROP NUMBER, INTERCEPTION STORAGE, MAXIMUM ROOT DEPTH, MAXIMUM
AREAL COVERAGE, SURFACE CONDITION AFTER HARVEST, RUNOFF CURVE NUMBER (AMC
II), USLEC, WFMAX (FORMAT 18, 3F8.0, 18, 3(1x, 13), 3(1x, F3.0) , F8.0) ]
CARD COLUMN
1 23456789m 2345678921 2345678931 2345678941 234567895J 2345678961 234567897J 2345
67898
1 0.05 45.0 85.0 3 86 78 82 0 0 NOTE: USLEC
and WFMAX
not required
CARD 7: Interception storage, maximum active root depth, maximum areal
coverage, surface condition after harvest and runoff curve numbers
for fallow, cropping, and residue fraction of the growing season.
The crop interception storage (CINTCp) is the amount of water a
plant canopy can retain before through-fall occurs. For peanuts, a
range of 0.0 to 0.15 cm is provided from Table 7. Because most
crops never obtain maximum density, the value estimated will be less
than maximum and 0.05 is estimated and entered for CINTCP.
The maximum active crop rooting depth (AMXDR) is a measure of
penetration of the active fraction of the total crop rooting depth.
From Table 8, a range of 30 to 60 cm is provided and a mean of 45.0
cm is estimated and entered for AMXDR.
The maximum areal coverage (COVMAX) is the amount of ground
cover afforded by the crop. Very little information is available on
110
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cover afforded by crops and Equation 32 provided by Williams in
Section 4 is used to estimate the ground cover from a similar row
crop, soybeans. The equation requires the Leaf Area Index of the crop
and from CREAMS (29) a value of 3.00 is provided. By substituting
into the Williams equation, a value of 0.95 (or 95%) is estimated.
This value is for a somewhat ideal growing crop; therefore, 0.85 is
estimated to reflect non-ideal conditions and entered for COVMAX as a
percentage, 85.
The surface condition after harvest (ICNAH) is a reflection of
the management practices that are conducted for the crop and are
either cropping, fallow, and/or residue (with corresponding values
of 1, 2, or 3); for peanuts, the residue value of 3 is entered for
ICNAH.
The curve numbers (CN) associated with the simulation are a
reflection of soil type, land treatment activities, and management
practices. Peanuts are row crops grown in straight rows on soils
that are in good hydrologic condition. The Norfolk sandy loam soil
has a hydrologic class of B and is in good hydrologic condition. A
curve number of 78 is estimated for peanuts during the cropping
season from Table 9. A curve number of 86 is estimated for the
fallow condition during the growing season. The residue condition
is a reflection of the amount of plant residue remaining on the
ground after harvest. For peanuts, less than 50% of the ground is
covered. The CREAMS manual (29) suggests, for residue coverage of
33%, that the curve numbers from the fallow and cropping condition be
averaged to estimate the residue curve number. For the Dougherty
Plain, 82 is estimated. The three curve numbers are entered for CN
fallow, cropping, and residue.
[ CARD 8.0: NUMBER OF CROPPING PERIODS (FORMAT 18) ]
28
CARD 8: Number of cropping periods (NCPDS)
This is simply the inclusive time between the starting and
ending date of the simulation and 1977 - 1950 = 28.
[ CARD 9.0: DAY, MONTH, YEAR CROP EMERGENCE; DAY, MONTH, YEAR CROP MATURA-
TION; DAY, MONTH, YEAR CROP HARVEST; CROP NUMBER GROWING IN CURRENT
PERIOD (FORMAT 2x, 312, 2x, 312, 2x, 312, 18) ]
100450 201050 011150 1
100451 201051 011151 1
100452 201052 011152 1
100453 201053 011153 1
100454 201054 011154 1
100455 201055 011155 1
111
-------
100456
100457
100458
100459
100460
100461
100462
100463
100464
100465
100466
100467
100468
100469
100470
100471
100472
100473
100474
100475
100476
100477
CARD 10
201056
201057
201058
201059
201060
201061
201062
201063
201064
201065
201066
201067
201068
201069
201070
201071
201072
201073
201074
201075
201076
201077
011156 1
011157 1
011 158 1
011159 1
011 160 1
011161 1
011 162 1
011 163 1
011 164 1
011165 1
011166 1
01 1 167 1
01 1 168 1
011169 1
011 170 1
011171 1
01 1 172 1
011173 1
011174 1
011175 1
011176 1
011177 1
.0: PTITLE (FORMAT 20A4)
PESTICIDE PROPERTIES
[ CARD 11.0; NUMBER OF APPLICATIONS (FORMAT 18) ]
28
[ CARD 12.0; DAY, MONTH, YEAR OF APPLICATION; RATE OF APPLICATION; DEPTH
OF INCORPORATION (FORMAT 2x, 312, 2F8.0) ]
CARD COLUMN
1 2345678901 2345678921 2 3456789.31 23456789-41 234567895J 234567896^1 23456789^71 2345
67898 ~ — — —
010450
010451
020452
010453
010454
010455
010456
210457
010458
060459
160460
250461
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
.0
.0
5,
5,
5,
5
5,
5,
5,
5,
5,
2.0
2.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
5.0
5.0
5.0
NOTE: Typical application
April 1, deviation is a
reflection of rainfall on
or near April 1 .
112
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240462
010463
010464
120465
010466
010467
010468
010469
170470
010471
010472
160473
210474
070475
050476
110477
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
5.0
CARD 12: Pesticide application timing and incorporation
The timing of pesticide application (APD) is a reflection of
rainfall, soil moisture, and label/management recommendations.
Estimating the timing of pesticide application involves evaluating
the rainfall record for days when the soil moisture is suitable for
a tractor or other equipment to operate in the field and to maintain
proper management practices. The guidance essentially indicates not
to apply the pesticide just before, during, or immediately after a
rainfall event.
The depth of incorporation (DEPI) is a function of the pesticides
formulation and management. The pesticide used in the Dougherty
Plain is a granular formulation incorporated at the time of planting.
The guidance provided from Table 17, suggests that a depth of 5 cm
is appropriate for this type of application.
[ CARD 13.0: PESTICIDE APPLICATION MODEL (FORMAT 18) ]
1
[ CARD 14.0; STITLE (FORMAT 20A4) ]
SOIL PROPERTIES
[ CARD 15.0: CORE DEPTH, UPTAKE EFFICIENCY FACTOR, NUMBER OF COMPARTMENTS,
BULK DENSITY, THETA, PARTITION FLAGS, AND SOIL HYDRAULICS (FORMAT 2F8.0,
518) ]
165.0
1 .0
33
113
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CARD 15: Core depth, uptake efficiency factor and number of compartments
The core depth (CORED) is a reflection of the depth from land
surface to the top of the water table and is highly variable from area
to area. Becaxise most pesticides have been found in superficial aqui-
fers, a core depth of 300 cm or less is recommended.
The uptake efficiency factor (UPTKF) is an estimate of the mass
of pesticide taken by the plant in relation to its transpiration
rate. For initial estimates, it is assumed that the uptake is equal
to the transpiration rate, and a value of 1.0 is entered for UPTKF.
(Equation 38 by Briggs (section 4.3.8) could be used initially.)
The number of compartments (NCOM2) reflects accuracy in the
numerical technique, array dimensioning, and computer run time (the
more compartments the higher the dimensioning and run time). In
Section 4, 30 was suggested as a minimum. A total of 33 is entered
for CORED.
[ CARD 16.0: NUMBER OF HORIZONS (FORMAT 18) ]
CARD 16: Number of horizons
The actual number of horizons (NHORIZ) in a soil profile are
determined by guidelines established within the Soil Conservation
Service. The goal in modeling is to combine, when possible, similar
horizons without changing a predominant characteristic, such as a
clay lens, for efficiency of data input. The first horizon is
generally the plow zone and its depth controls where runoff is
estimated. The plow zone in the Dougherty Plain is 15 cm and corre-
sponds to horizon 1. The next step is to locate similar soil horizons
with properties that can be combined (averaged) for simulation. The
(17 -30, 30 - 40), (40 - 58, 58 - 60), and (76 - 100, 100 - 106)
horizons, from Table 23, have common properties of organic carbon,
similar moisture holding characteristics, and do not have any confin-
ing layers. After combining similar horizons, a total of three below
the plow layer are categorized. A total of four horizons for the
total soil profile are designated and entered for NHORIZ.
[ CARD 17.0; HORIZON NUMBER, HORIZON THICKNESS, BULK DENSITY, DISPERSION
COEFFICIENT, PESTICIDE DECAY RATE, INITIAL SOIL WATER CONTENT, DRAINAGE
PARAMETER(FORMAT 18, 6F8.0) ]
CARD COLUMN
1 23456789m 234567892^1 23456789_31 2345678941 234567895J 2345678961 23456789^71 2345
67898
114
-------
1 15.0 1.68 0.0 0.0134 0.11
2 50.0 1.72 0.0 0.0134 0.13
3 50.0 1.60 0.0 0.0134 0.14
4 50.0 1.74 0.0 0.0134 0.16
CARD 17: Horizon thickness, initial soil water content
The thickness of the horizons (THKNS) are either divisions
reported by the Soil Conservation Service or by the technique
described for the number of horizons (CARD 16). The technique used
from CARD 16 provides that the first horizon is 15 cm, the second is
25 cm, the third is 20 cm, and the fourth is 105 cm. The total
thickness must not be greater than the total core depth of 165 cm as
designated in CARD 15). The remaining parameters for this card,
including 17A, are taken from Table 23.
The initial soil water content (THETO) is a reflection of the
condition in which the field exists at the start of the simulation.
Unless the condition is known, an initial condition of field capacity
is assumed. These initial conditions will dampen out after a few
days of run time.
[ CARD 17.A: FIELD CAPACITY, WILTING POINT, SORPTION COEFFICIENT, ORGANIC
CARBON CONTENT (FORMAT 8x, 4F8.0) ]
0.1 1
0.1 3
0.14
0.16
0.010
0.050
0.090
0.110
0.80
0.40
0.20
0.10
NOTE: Organic carbon content not
required for this data set.
[ CARD 18.0; INITIAL LEVEL PESTICIDE INDICATOR AND CONVERSION FLAG FOR
INITIAL RESIDUES (FORMAT 218) ]
CARD 18: Initial level indicator
The initial level indicator (ILP) provides for initial conditions
where existing levels of pesticide exist and these levels are entered
in mg kg~^ or kg ha~^ . For the initial condition for the Dougherty
Plain example, an assumption of non-existing background levels is made
and a 0 is entered for ILP.
[ CARD 19.0: HYDROLOGIC SUMMARY INDICATOR, TIME STEP OF OUTPUT, FREQUENCY
OF SOIL COMPARTMENT REPORTING; PESTICIDE SUMMARY INDICATOR, TIME STEP OF
OUTPUT, FREQUENCY OF SOIL COMPARTMENT REPORTING; PESTICIDE CONCENTRATION
PROFILE INDICATOR, TIME STEP OF OUTPUT, FREQUENCY OF SOIL COMPARTMENT
REPORTING (FORMAT 3(4x, A4, 4x, A4, 18) ]
115
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WATR YEAR 1
[ CARD 20.0: NUMBER OF PLOTS (FORMAT 18) ]
1
[ CARD 21.0: IDENTIFIER OF TIME SERIES, PLOTTING MODE, ARGUMENT OF
VARIABLE, AND CONVERSION CONSTANT (FORMAT 4x, A4, 4x, A4, 18, F8.0) ]
RZFX TCUM 10
6.5 GENERAL CALIBRATION AND EXPOSURE ASSESSMENT
Calibration, according to Donigian, A. S., Jr. et al., 1984.
(Application of Hydrologic Program-FORTRAN (HSPF) in Iowa Agricultural
Watersheds - EPA 600/S3-83-069), is an iterative procedure of para-
meter evaluation and refinement by which simulated and observed values
of interest are compared. It is required for parameters that cannot
be deterministically evaluated for a given site. Fortunately, the
majority of PRZM parameters do not fall in this category. Calibration
should be based on several years of simulation (3 to 5 is optimal)
in order to evaluate parameters under a variety of climatic, soil
moisture, and land use conditions. Calibration should be accomplished
using years with normal to above normal precipitation. Calibration
on years that are below normal may bias the parameter values because
the parameters may not represent the processes occurring during wet
periods. Calibration should result in parameter values that produce
the best overall agreement between simulated and observed values
throughout the calibration period (using parameter values within
expected boundary ranges).
Calibration for runoff models includes the comparison of yearly
and monthly runoff totals and individual storm events. The calibra-
tion should first be done with hydrology (runoff), followed by erosion
(sediment) and then chemical (pesticide). A calibration scheme from
Donigian, et al., is outlined below.
1. Estimate individual values for all parameters.
2. Perform hydrologic calibration run.
3. Compare simulated yearly and monthly values with observed data.
4. Adjust hydrologic parameter values (and initial conditions if
necessary) to improve agreement between yearly and monthly
values.
5. Repeat steps 2 and 3 until satisfactory agreement is reached.
116
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6. Compare simulated and selected individual storm events.
7. Adjust hydrologic calibration parameters to improve agreement
for individual storm events.
8. Repeat step 7 until satisfactory agreement is reached while
maintaining agreement in the yearly and monthly runoff simula-
tion.
9. The same procedure is followed for sediment and pesticide
calibration. In the case of pesticide leaching, the observed
data will also include soil profile concentrations.
At the conclusion of the above steps, PRZM is calibrated to the
field being simulated under the land conditions in effect during the
calibration period. The validation exercise on other years of observed
data can be initiated or long term simulations for assessment can be
accomplished.
Many times the user will not have the luxury of observed data
to calibrate against and in some cases the assessment may warrant the
collection of field data.
6.6 MODEL CALIBRATION WITH LIMITED DATA
The first task in assessing the correct hydrologic parameters
for PRZM is to establish a water balance that is representative for
the area simulated on an annual basis. The balance specifies the
ultimate destination of incoming precipitation and is written as:
PRECIPITATION - EVAPOTRANSPIRATION - RUNOFF - CHANGE (44)
IN STORAGE = DEEP PERCOLATION.
In addition to the input meteorologic data series, the parameters
that govern this balance are PFAC, ANETD, THEFC, THEWP, CN, and AMXDR.
If a series of rainfall data and knowledge of the potential evapotrans-
piration or deep percolation exist, a representative water balance can
be obtained by varying the above parameters.
The first parameter that should be adjusted is PFAC (pan factor) .
The value obtained from Figure 4 is relative and can be varied
plus or minus 20%. The annual ET in our example is 60 to 80 cm per
year and the annual runoff is 15.0 - 20.0 cm. The first step is to
obtain several years of precipitation record that are average to above
average for the area (1970, 1971, 1975, and 1976). The first calibra-
tion run produced 59.0, 65.0, 65.0, and 61.0 cm ET, which is low for
the area. The pan factor should be increased because the simulated ET
was low. Adjusting the value from 0.75 to 0.90 produces ET of 62.0,
70.0, 69.0, and 65.0 cm, which is higher than the first calibration
117
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run but still low for the area. The next parameter to adjust is ANETD
(evaporation). Increasing the value from 25.0 cm to 30.0 cm produces
65.0, 72.0, 70.0, and 67.0 cm ET. The runoff is 26.0, 21.0, 18.0, and
17.0 cm year"1. The water balance for ET and runoff appears represen-
tative.
Deep percolation results are 48.0, 49.0, 43.0, and 50.0 cm.
Simulated calibration runs for [ET + RUNOFF + DLST + DEEP PERC =
PRECIP (about 127 cm)] are representative of the region's hydrologic
response. If it were not, an adjustment of the storage would be
required with THEFC and THEWP varied. The difference between the two
is the amount of water stored. To increase the storage, make more
water available for ET, and allow less percolation, a larger difference
is created. The length of time/runs for calibration is largely based
on user experience. An experienced user would have a better estimate of
hydrologic parameters (i.e., ANETD) and a shorter calibration exercise
would be expected.
6.7 EXPOSURE ASSESSMENT, SENSITIVITY ANALYSIS, AND PRODUCTION RUNS
Many input parameters can be changed to affect the results of
PRZM simulations. Sensitivity analyses should be made for those
parameters that will have the greatest impact. Two categories of
parameters—transport and supply—affect the amount of pesticide
that leaches below a given depth. Transport parameters affect the
movement of contaminants whereas supply parameters govern the quan-
tity of the contaminant present for movement. The dominant transport
and supply parameters for sensitivity testing with PRZM are provided
in Table 25.
Table 25. PRZM Sensitivity Testing Parameters
Category Parameter
Transport KD (Adsorption Coefficient)
BD (Bulk Density)
THEFC (Field Capacity)
THEWP (Wilting Point)
CN (Curve Number)
Supply RA (Application Rate)
KS (Decay Rate)
AL (Active Layer—Root Zone)
118
-------
The eight parameters provided in Table 25 will have an impact
on the leaching of a chemical. In the majority of analyses, all of
these values will not have to be varied. The application rate is
usually fixed by specific label recommendations for any particular
pesticide. The label rate would have to be varied only in an
exercise where investigations of alternative management practices
are indicated. Bulk density ranges from 1.0 - 2.0, with 1.4 - 1.6
g cm~3 commonly reported. In any case, its value has minimal effect
on pesticide leaching. The curve numbers for a given soil series are
generally fixed and usually do not require extensive variation. The
decay rate, partition coefficient, soil moisture content, and depth of
active layer (root zone) are very sensitive parameters that affect
leaching. These parameters must be investigated to obtain a range of
pesticide movement. Minimum, mean, and maximum values can be used in
sensitivity testing. This same logic applies to many of the PRZM
parameters. Figure 14 provides an example sensitivity testing scheme
using the degradation rate constant, KS, as the parameter being varied.
TEST 1 .0
HORIZON
FIELD
CAPACITY
WILTING
POINT
KD KS
ROOT
ZONE
MASS (g ha~1 )
LEACHED PASSED
ROOT ZONE
1
2
3
4
1
2
3
4.
1
2
3
4
0
0
0
0
.110
.130
.140
.160
0
0
0
1 1
.010
.050
.090
.000
0
0
0
0
.80
.40
.20
.10
0
0
0
0
.0268
.0268
.0268
.0268
45.0
TEST 2.0
0
0
0
0
.1 10
.130
.140
.160
0
0
0
1 1
.010
.050
.090
.000
0
0
0
0
.80
.40
.20
.10
0
0
0
0
.01
.01
.01
.01
34
34
34
34
45.0
TEST 3.0
0.1 10
0.130
0.140
0.160
0.010
0.050
0.090
1 1 .000
0.80
0.40
0.20
0.10
0.0067 45.
0.0067
0.0067
0.0067
.Oa 0.1b 40.Oc
60.Oa 1.Ob 228.Oc
114.Oa 6.0b 340.Oc
amean value 28 years, blowest year, chighest year
Figure 14. Example sensitivity testing scheme for KS.
119
-------
Sensitivity testing provides a means to investigate the ranges
of pesticide mass (g ha~^) that will move below a certain depth.
An important issue in determining the leaching potential of a
specific pesticide is its frequency or probability of leaching.
Continuous simulation models that generate time series data provide
a technique to evaluate exposure to various magnitudes and durations
of chemical mass fluxes. Measures of exposure levels include the
frequency (or percent of the time) specific conditions exist (for a
chemical).
A period of record of at least 20 years may be required to derive
a probability statement. The calibrated model and the available 28-
year meteorologic record from the example can be used to derive
probability statements about the events simulated.
The probability statement is estimated from the cumulative
frequency distribution of the results. Several steps are required
to complete the assessment after running the model.
STEP 1: Prepare a column of ranges of mass (g ha~1) leaving the
45.0 cm root zone.
EXAMPLE:
0
20
40
60
80
100
120
140
- 20
- 40
- 60
- 80
- 100
- 120
- 140
- 300
STEP 2: Calculate the frequency with which results fall within
each group from model results, the cumulative distribu-
tion, and cumulative frequency of the results.
YEAR
,-1
,-1
g ha-l year"1 LEACHING
PAST 45.0 cm ROOT ZONE
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
35.0
36.0
17.0
98.0
1 .0
25.0
16.0
80.0
92.0
35.0
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
228.0
28.0
27.0
16.0
16.0
70.0
87.0
83.0
31 .0
29.0
49.0
120
-------
1960 6.0 1975 49.0
1961 37.0 1976 109.0
1962 8.0 1977 34.0
1963 8.0
CUMULATIVE FREQUENCY
RANGE DISTRIBUTION DISTRIBUTION DISTRIBUTION
0-20 8 8 0.29
20 - 40 10 18 0.64
40-60 2 20 0.71
60-80 2 22 0.79
80 - 100 4 26 0.93
100 - 120 1 27 0.96
1 20 -' 300 1 28 1 .00
STEP 3: Prepare a plot of grams leached below 45.0 cm versus
cumulative frequency distribution - Figure 15.
The cumulative frequency distribution (CDF)
enables a probability statement of leaching potential
to be made and provides several important details of
exposure information. The 50% value means that half
the time less than 40 g ha~1 yr~^ will leach below the
root zone. The return interval (RT) sometimes called
recurrence or simple frequency is calculated using the
equation:
RT = 1 (42)
1 - P(X _<^ x)
where RT = return interval, years
P(X < x) = probability of an event equal to
or less than x occurring once in RT
years
The return interval calculated from the 50% level is
2.0 years (or 40 g ha~^ will leach past the root zone
every two years). For further demonstration suppose
a risk level is set at the 80% level (or a return
interval of 5 years). The annual mass leaching
beyond the root zone with a 5 year RT is 80.0 g ha~^.
Continuous simulation modeling can also be used
to evaluate different management practices and their
effect on the leaching potential. Consider for example
a change in the timing of pesticide application. The
increase or decrease in risk expected from altering
the timing of the application can be investigated
using the procedure just outlined. The pesticide
121
-------
1.0-1
0.0
180
240
Frequency of pesticide (g-ha 1year 1)
leached below 45.0 cm
Figure 15. Cumulative frequency distribution of pesticide
leaving root zone.
122
-------
application will be delayed approximately 30 days,
The results are presented below.
YEAR
g ha~1 year"1 LEACHING
PAST 45.0-cm ROOT ZONE
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
26.0
39.0
19.0
113.0
1 .0
21.0
17.0
90.0
66.0
49.0
18.0
39.0
29.0
90.0
149.0
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
26.0
35.0
22.0
18.0
83.0
101 .0
76.0
45.0
19.0
56.0
19.0
119.0
43.0
RANGE
DISTRIBUTION
CUMULATIVE
DISTRIBUTION
FREQUENCY
DISTRIBUTION
0
20
40
60
80
100
120
- 20
- 40
- 60
- 80
- 100
- 120
- 150
8
7
4
2
3
3
1
8
15
19
21
24
27
28
0.29
0.54
0.68
0.75
0.86
0.96
1 .00
For our example, the amount of pesticide leaving the root zone
at the 80% (or return interval of 5 years) is 80.0 g ha~1. The risk
apparently has not been decreased because the frequency curves for
the 80% values are the same. This finding does not produce results
that would be expected (reduced risk), because delaying the application
should result in more removal (i.e uptake, etc.). Analysis of the
data, however, indicates that uptake is increased only 1% by delaying
the application. This example did not include a potential faster rate
of decay due to higher soil temperatures (by delaying the application).
The example does demonstrate the utility of continuous simulation
modeling in assessing management alternatives.
6.8 DOCUMENTATION AND REPORTING OF RESULTS
The degree, or extent, of pesticide interaction within a hydro-
logic response depends on a variety of critical compound and site
123
-------
characteristics. These critical characteristics are essential to
understanding/determining the leaching potential of a pesticide. The
ultimate goal of such understanding is to perform consistent assess-
ments under a variety of circumstances. A reliable investigation of
pesticide movement through the unsaturated zone requires the assess-
ment of soils, hydrologic site characteristics, climatic information,
agronomic practices, and pesticide properties/interactions. Many
times such information will have to be either estimated or obtained
through a variety of sources. A thorough documentation of the infor-
mation used/estimated is required for a successful application of
PRZM. Documentation provides a record of data sources, promotes ease
of operation for other users, decreases operational learning curve
requirement (increases user efficiency), and serves as a quick reference
for future use. Figure 16 provides an example of an assessment data
sheet for PRZM simulations.
Date of Assessment:
Note:
Can be used separately
or attached to hard
copy output.
saturation, and drainage alpha
Site:
Site Characteristics:
Investigator:
Compound Name:
Compound Characteristics:
Critical Hydrology Parameters:
hydrologic group
depth of horizons
depth of root zone
depth of unsaturated zone
evapotranspiration extraction
number of horizons
field capacity, wilting point,
meteorologic station
crop and cropping information
Critical Pesticide Parameters:
sorption constant
decay rate
bulk density
depth of incorporation
application rate
application date
Sources of Information:
Calculation of Options Used for Parameter Estimation:
Exposure Assessment Methodology:
Figure 16. Documentation data sheet for a PRZM assessment of the
unsaturated zone.
124
-------
APPENDIX A:
PRZM DEVELOPMENTAL REFERENCES
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Equations. Academic Press, New York.
2. Anderson, M. P. CRC Grit. Rev. Environ. Control. 1979, 9 (2),
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389-395.
5. Burkhard, N., Guth, J. A. Pestic. Sci. 1981, 12, 37-44.
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and Technical Background, Metals, Inorganics, Pesticides, and
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Report No. EPA-440/4-79-029a. April 1979.
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Pesticide Leaching Threats to Ground Water. (In press.)
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206, 831-832.
9. Dagan, G., Bresler, E. Soil Sci. Soc. Amer. J. 1979. 43, 461-465.
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EPA-660/2-75-009. 1975.
11. Donigian, A. S., Jr., Imhoff, J. C., Bicknell, B. R., Baker,
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12. Doorenbes, S. and Pruitt, W. O. Guidelines for Predicting Crop Water
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15. Freeze, R. and Cherry, J. A. Ground Water. Prentice-Hall, M. J.
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16. Gunther, F. A., Westlake, W. E., Jaglan, P. S. Res. Rev. 1968, 20,
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17. Gureghian, A. B., Ward, D. S., Cleary, R. W. J. Hydrol. 1979. 41,
253-278.
18. Haith, D. A., Loehr, R. C., (Eds.) "Effectiveness of Soil and Water
Conservation Pratices for Pollution Control." U. S. Environmental
Protection Agency, Athens, GA. Report No. EPA-600/3-79-106. 1979.
19. Hamon, W. R. J. of the Hydraulics Div. ASCE. 1961. 87(HY3), 107-120.
20. Hebb, E. A. and Wheeler, W. B. J.Environ. Qual. 1978, 7, 598-601.
21. Hoffman, J. F., Lubke, E. R. "Ground-Water Levels and Their
Relationship to Ground-Water Problems in Suffolk County, Long
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Water Resources Commission Bulletin GW-44. 1961.
22. Hough, A., Thompson, T. J., Taner, W. J. J. Nematology. 1975,
7, 212-215.
23. INTERA Environmental Consultants. "Mathematical Simulation of
Aldicarb Behavior on Long Island." U. S. Environmental Protection
Agency, Office of Pesticide Programs, Washington, DC. Contract
80-6876-02. 1980.
24. Jones, R. L. "Movement and Degradation of Aldicarb Residues in Soil
and Ground Water." Presented at the CETAC Conference on MultLdisci-
linary Approaches to Environmental Problems, Crystal City, VA,
Nov. 6-9, 1983.
25. Jones, R. L., Rao, P. S. C., Hornsby, A. G. "Fate of Aldicarb in
Florida Citrus Soil 2. Model Evaluation." Presented at the Conference
on Characterization and Monitoring of the Vadose (Unsaturated) Zone,
Las Vegas, NV, Dec. 8-10, 1983.
126
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26. Jury, W. A., Grover, R., Spencer, W. F., Farmer, W. J. Soil Sci.
Am. Proc. 1980, 44, 445-450.
27. Karickhoff, S. W., Brown, D. S., and Scott, T. A. 1979.
Water Research. 13, 241 - 248.
28. Kenaga, E. E. and Goring, C. A. I. Relationship Between Water
Solubility, Soil-Sorption, Octanol-Water Partitioning and Bio-
concentration of Chemicals in Biota. Presented at American Society
for Testing and Materials, Third Aquatic Toxicology Symposium, New
Orleans, Louisiana. 1978.
29. Knisel, W. G., Ed. "CREAMS: A Field-Scale Model for Chemicals,
Runoff, and Erosion, from Agricultural Management Systems", U. S.
Department of Agriculture, Washington, DC. Report No. 26. July
1980.
30. Leistra, M. Plant and Soil. 1978. 49, 569-580.
31. Linsley, R. K., Jr., Kohler, M. A., and Paulhus, J. L. H. Hydrology
for Engineers. McGraw Hill, New York. 1975.
32. Menzel, R. G. Enrichment Ratios for Water Quality Modeling. 1980.
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33. Mockus, V. "Estimation of Direct Surface Runoff from Storm
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August 1972.
34. Mulkey, L. A., Falco, J. W. "Methodology for Predicting Exposure
and Fate of Pesticides in Aquatic Environments," In: "Agricultural
Management and Water Quality", Schaler, F. W., Bailey, G. W., Eds.
Iowa State University Press, Ames, IA. 1983.
35. Onishi, Y., Brown, S. M., Olsen, A. R., Parkhurst, M. A.,
Wise, S. E., Walters, W. H. "Methodology for Overland Flow
and Instream Migration and Risk Assessment of Pesticides."
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EPA-600/3-82-024. May 1982.
36. Nash, R. G. Dissipation Rate of Pesticides from Soils. 1980. In:
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127
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37. Pacenka, S., Porter, K. S., "Preliminary Assessment of the
Environmental Fate of the Potato Pesticide, Aldicarb, In Soil
and Ground Water Eastern Long Island, New York", subcontract
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13, 348-354.
39. Peoples, S. A., Maddy, K. T., Cusick, W., Jackson, T., Cooper, C.,
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40. Roberts, J. R., Greenhalgh, K. , Marshall, W. K. "Fenitrothiori:
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Pesti. Sci. 1978, 9, 286-292.
45. Smelt, J. H., Leistra, M., Norbert, W., Houx, H. , Dekker, A.
Pesti. Sci. 1978, 9, 293-300.
46. Smith, C. N. and Carsel, R. F. " Foliar Washoff of Pesticides (FWOP)
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51. Union Carbide Corporation. "Temik Aldicarb Pesticide: A Scientific
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129
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-------
APPENDIX C:
EXAMPLE DATA SETS
EXAMPLE DATA SET LONG ISLAND, N.Y. ALDICARB PESTICIDE ON POTATOES
010177
123179
0.70
9.55
12.23
0
1
1
3
100577
100578
100579
6
150477
100677
150478
100678
150479
100679
0
225.0
2
1
2
0
WATR
0
0.45
10.53
10.95
0.0
220877
220878
220879
2.24
1 .35
2.52
2.02
3.19
2.52
1 .0
30.0
0.24
195.0
0.24
0
MNTH
n x u.n.wjLjv-'v.
1
1 1 .77
9.81
30.0
100977
100978
1 00979
T> T^CT'Tr'Tr
c HjO J. J.^ JLi
1 .0
1 .0
1 .0
1 .0
1 .0
1 .0
QHTT PDf
OWJ..LI irr\A
45
1 .62
0.10
1 .62
0.10
1
3 j. jr rt 1*^11*1 ILX ijj\.o
1 5.0 1 3
13.07 14.16 14.72 14.44 13.48
9.21
40.0 3 77 67 72
1
1
IDE'PT'TPQ — — — — — —
0000
0.0 0.0139 0.24
0.14
0.0 0.0009 0.24
0.07
PEST MNTH 1 CONG MNTH
181
-------
EXAMPLE DATA SET WATKINSVILLE, GEORGIA
010174 123174
ATRAZINE PESTICIDE ON CORN
0.80
1
0.28
1
1
1
290474
0.45
0.28
0.25
160974
— niLJK.^.
0
1 .0
60.0
291074
Jl-iWU I ft\
10.0
1 .00
90.0
Krti-iEj i .CJ.K.O
1
2.8
0.00
1
3 86 78 82
.50 .20
•nTPornT/~>Tnir TIT\ r> 7\ MT?
-------
EXAMPLE OUTPUT (HYDROLOGY FILE) WATKINSVILLE DATA SET
********************************
* PESTICIDE ROOT ZONE MODEL *
* VERSION 1 (PRZM) *
* *
* 1 MAR., 1984 *
* *
DEVELOPED BY:
U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
ATHENS ENVRIONMENTAL RESEARCH LABORATORY
ATHENS, GA. 30613
404-546-3138
AND
ANDERSON-NICHOLS
2666 EAST BAYSHORE ROAD
PALO ALTO, CA. 94303
415-493-1864
THIS RUN WAS MADE AT ** 11:30:58 ON 01-APR-84 **
EXAMPLE DATA SET WATKINSVILLE, GEORGIA ATRAZINE ON CORN
SIMULATION START DATE (DAY-MONTH-YEAR) 19 APR., 74
SIMULATION END DATE (DAY-MONTH-YEAR) 31 DEC., 74
**************** HYDROLOGY PARAMETERS
HY£R£LOGY .AND £ED_II4E£T_R£LATED_PA_RA_METERS^
PAN COEFFICIENT FOR EVAPORATION 0.8000
FLAG FOR ET SOURCE ( 0=EVAP, 1 =TMEP, 2=EITHER) 0
DEPTH TO WHICH ET IS COMPUTED YEAR-ROUND (CM) 10.00
SNOW MELT COEFFICIENT (CM/DEG-C-DAY) 0.4570
INITIAL CROP NUMBER 1
INITIAL CROP CONDITION 1
USLE "K" PARAMETER 0.2800
USLE "LS" PARAMETER 0.2800
USLE "P" PARAMETER 1 .000
FIELD OR PLOT AREA (HA) 1 .000
AVERAGE EROSIVE STORM DURATION 2.800
183
-------
MAXIMUM MAXIMUM SURFACE
CROP INTERCEPT ACTIVE MAXIMUM MAXIMUM CONDITION
NUMBER POTENTIAL ROOT DEPTH COVER WEIGHT AFTER AMC RUNOFF CURVE
(CM) (CM) (90%) (KG/M**2) HARVEST FALLOW CROP
0.2500
NUMBER
RESIDUE
66
82
92
60.00
90.00 0.0000
USLEC COVER MANAGEMENT
"C" FACTOR
FALLOW CROP RESIDUE
0.5000 0.2000 0.2000
I
II
III
72
86
94
60
78
90
CROP ROTATION INFORMATION
CROP
NUMBER
1
EMERGENCE
DATE
29 APR., 74
MATURATION
DATE
16 SEPT., 74
HARVEST
DATE
29 OCT., 74
***************** PESTICIDE PROPERTIES *******************
PESTICIDE APPLICATION INFORMATION
APPLICATION
DATE
11 MAY , 74
PESTICIDE
APPLIED
(KG/HA)
3.800
INCORPORATION
DEPTH
(CM)
0.000
JPLANT £ESTIC_IDE PARAMETERS
MODEL UTILIZED (1 =SOIL,2=LINEAR,3=EXPONENTIAL)
184
-------
*****************
SOIL PARAMETERS
******************
GENERAL SOIL INFORMATION
CORE DEPTH (CM) 175.0
TOTAL HORIZONS IN CORE 3
TOTAL COMPARTMENTS IN CORE 35
PLANT UPTAKE EFFICIENCY FACTOR 0.1000E-01
THETA FLAG (0=INPUT,1=CALUCLATED) 0
PARTITION COEFFICIENT FLAGE (0=INPUT, 1 CALCULATED) 0
BULK DENSITY FLAG (0=INPUT, 1 CALCULATED) 0
SOIL HYDRAULICS MODULE (0=HYDR1,1=HYDR2) 1
SOIL HORIZON INFORMATION
HORIZON
1
2
3
THICKNESS
(CM)
10.00
50.00
115.00
PARTITION
COEFFICIENT
(CM**2/G)
2.240
2.240
2.240
PESTICIDE ,
BULK DECAY
DENSITY RATE i
(G/CM**3) (/DAY)
1.600 0.3700E-01
1 .600 0.3700E-01 i
1.600 0.3700E-01
DISPERSION ORGANIC
COEFFICIENT CARBON
(CM**2/DAY) (%)
0.0000
0.0000
0.0000
INITIAL FIELD WILTING
SOIL CAPACITY POINT
WATER DRAINAGE WATER WATER
CONTENT PARAMETER CONTENT CONTENT
(CM/CM) (/DAY)
0.2420 2.200
0.2420 2.200
0.2420 2.200
(CM/CM) (CM/CM)
0.2420 0.1450
0.2420 0.1450
0.2420 0.1450
OUTPUT
WATR
PEST
_ £ARAMETERS
TIME STEP
YEAR
MNTH
LAYER FREQ
1
5
£LOT_FILE JCNFORMATION
NUMBER OF PLOTTING VARIABLES
TIMSER NAME MODE
TPST TCUM
1
ARGUMENT
13
CONSTANT
1 .000
185
-------
************************
* ANNUAL WATER OUTPUT *
* *
* DATE: 31 DEC., 74*
************************
CURRENT CONDITIONS
ALL HYDROLOGY UNITS ARE CM OF WATER
SEDIMENT UNITS ARE METRIC TONNES
NUMBERS IN PARENTHESES ARE SOIL WATER CONTENTS
CROP NUMBER 1
FRACTION OF GROUND COVER 0.0000
INTERCEPTION POTENTIAL 0.0000
DEPTH TO WHICH ET IS EXTRACTED(CM) 10.00
FLUXES AND STORAGES FOR THIS PERIOD
CANOPY
SURFACE
SOIL
LAYERS
HORIZON
1
1
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
PREVIOUS
STORAGE
0.0000
THRU FALL
70.95
COMPART-
COMPART- PREVIOUS
MENTS STORAGE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
1 .210
1 .210
1 .210
1 .210
1 .210
1 .210
1 .210
1 .210
1 .210
1 .210
1.210
1 .210
1 .210
1 .210
1.210
1 .210
1 .210
1 .210
( .242)
( .242)
( .242)
(.242)
( .242)
(.242)
( .242)
( .242)
(.242)
(.242)
(.242)
(.242)
( .242)
(.242)
( .242)
(.242)
(.242)
(.242)
PRECIPITA- EVAPORA- HRUFALL
TION TION
74.40 3.446 0.95
RUNOFF INFIL-
TRATION
14.45 56.33
LEACHING TRANSPIRA- LEACHING
INPUT
56
35
27
23
20
18
17
16
15
14
14
13
13
13
13
1 3
13
13
.50
.83
.28
.71
.80
.09
.12
.05
.37
.81
.34
.98
.75
.67
.60
.54
.48
.43
20
8.
3.
2.
2.
1 .
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
TION
.67
527
534
845
047
491
9849
5887
4715
3778
2747
1504
0000
0000
0000
0000
0000
0000
OUTPUT
38.10
29.04
23.35
19.12
16.16
14.68
13.64
13.03
12.55
12.15
11 .85
1 1 .68
11.66
1 1 .64
11 .63
11 .61
11.59
1 1 .57
CURRENT
STORAGE
0.0000
CURRENT
STORAGE
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.206
.212
.215
.218
.221
.223
.225
.227
.229
.229
.229
.229
.229
.229
.229
.229
.229
.229
( .241 )
(.242)
( .243)
( .244)
(.244)
(.245)
( .245)
( .245)
( .246)
(.246)
( .246)
(.246)
( .246)
( .246)
(.246)
(.246)
(.246)
( .246)
186
-------
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.210
.210
.210
.210
.210
.210
.210
.210
.210
.210
.210
.210
.210
.210
.210
.210
.210
( .242)
(.242)
( .242)
( .242)
( .242)
(.242)
( .242)
( .242)
( .242)
(.242)
( .242)
( .242)
( .242)
(.242)
( .242)
( .242)
( .242)
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
.57
.55
.54
.52
.50
.48
.47
.45
.44
.42
.41
.39
.38
.36
.35
.33
.32
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
1 1
1 1
1 1
1 1
1 1
1 1
11
1 1
11
1 1
1 1
1 1
1 1
1 1
1 1
1 1
11
.55
.54
.52
.50
.48
.47
.45
.44
.42
.41
.39
.38
.36
.35
.33
.32
.31
1 .228
1 .228
1 .227
1 .227
1 .227
1 .226
1.226
1 .226
1 .226
1 .225
1 .225
1 .225
1 .224
1 .224
1 .224
1 .223
1 .223
( .246)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
( .245)
SUMMARY; FLUXES
TOTAL SEDIMENT ERODED FROM SURFACE
TOTAL ET FROM PROFILE
RECHARGE BELOW ROOT ZONE
MATERIAL_BALA_NC_E
WATER BALANCE ERROR
CUMULATIVE ERROR
3.383
41 .96
13.75
-.1 103E-05
•.3838E-04
167
-------
APENDIX D:
JULIAN DAY CALENDAR (PERPETUAL)
Day
1
2
3
4
5
6
7
8
9
10
1 1
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Jan.
1
2
3
4
5
6
7
8
9
10
1 1
12
13
14
15
16
17
1 8
19
20
21
22
23
24
25
26
27
28
29
30
31
Feb.
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
Mar.
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
Apr.
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
1 1 1
1 12
113
114
115
116
117
118
119
120
May
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
Jun.
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
Jul.
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
Aug.
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
Sep.
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
Oct.
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
Nov.
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
Dec.
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
188
-------
APPENDIX E:
PROGRAMMER'S GUIDE
PROGRAM STRUCTURE
PRZM is structured so that a single main routine calls every subrou-
tine in sequence with one exception. The flow chart provided in Figure
E-1 shows the order of the call sequence, read from left to right and top
to bottom. Only the tridiagonal matrix solving subroutine is not called
directly by the main program. Some subroutines (THCALC, KDCALC, EROSN,
and all output routines) may not be called in every circumstance depending
on user input requirements or specific simulation requirements (EROSN,
PESTAP). Either of the soil hydraulics routines, HYDR1 or HYDR2, may be
called, depending on the choice made in the input sequence. All values
passing in and out of subroutines are handled through COMMON blocks exept
those passed between SLPEST and TRDIAG. The flow chart provides a brief
description of the function of each routine.
Numerous comment lines in the code itself provide more detailed des-
cription of functions performed by each subroutine.
COMMON BLOCKS
PRZM uses few subroutine arguments and passes most information through
six COMMON blocks. Each block contains related parameters, fluxes, and
state variables. The COMMON blocks may be stored as files separate from
the PRZM code, and accessed by use of INCLUDE statements at the beginning
of the main program and each subroutine. Only the COMMON blocks necessary
for the execution of a subroutine are included in the subroutine. The six
COMMON blocks are:
1 ) HYDR - surface and soil hydrology related terms
2) PEST - pesticide fate, transport, and application
related terms
3) CROP - crop timing and growth related terms
4) MET - meteorologic related terms
5) MISC - miscellaneous terms including output flags and
time-keeping variables
189
-------
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190
-------
6) ACCUM - cumulative terms carried from one time step to the
next, primarily for outputting water and pesticide
summaries.
A complete list of all the model variables and parameters are contained
in Table E-1. The table gives (from left to right) variable name, internal
unit, (which may differ from the user input units for some input variables),
a variable description, a list of each subroutine in which it appears, the
COMMON block in which it appears and whether it is an input (I), output
(O), modified (M) or local (blank) variable.
PARAMETER STATEMENTS
Use of two PARAMETER statements in PRZM precludes the need for most
users to access the body of the model to make changes. The user need only
edit the PARAMETER statements and recompile the code to meet most system
requirements.
Array Sizes
The first PARAMETER statement used at the beginning of each subroutine
enables the user to easily change sizes of the most commonly utilized arrays.
Most of the arrays in PRZM are dimensioned by 1 of 3 parameters. These are
the number of compartments in the soil profile (NCMPTS), the number of
pesticide applications (NAPP), and the number of different cropping periods
(NC). By changing their values in the PARAMETER statement, all of the
arrays associated with these arguments in PRZM will automatically reflect
those changes. The PARAMETER statements can be stored in files separate
from the PRZM code. They are included at the beginning of each subroutine
by use of an INCLUDE statement. If INCLUDE statements are not used, then
the PARAMETER statement at the beginning of each subroutine must be edited
to avoid disrupting COMMON memory areas, if changes are desired in COMMON
array sizes. Chapter 4 suggests values for NCMPTS to both minimize computer
run-time and the effects of numerical dispersion. The values of NCMPTS and
NAPP must be at least one number larger than the number of compartments and
pesticide applications to be simulated. Values for NC and NAPP will depend
on the system simulated. Even using small values of these array dimensions,
smaller computers may require overlays or segmentation in order to fit the
program on a particular machine.
File Units
The second PARAMETER statement used at the beginning of each subroutine
defines the logical unit numbers associated with input and output files'.
Different machines have different requirements as to logical unit specifica-
tion. By equating the file unit number variable to the machine specific
number system in the PARAMETER statement, all occurrences of READ and WRITE
statements can be changed. The parameter list contains the parameters
FLMT, FLIN, FLWT, FLPS, FLTS and FLCN. Numbers supplied for these parame-
191
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation
Variable
A
AD
AD FLUX
ADS
AFIELD
AINF
AMXDR
ANETD
Units
day-1
day"1
g cm"2
day-1
mg kg"1
ha
cm
cm
cm
Type
Array
Array
Array
Array
Scalar
Array
Scalar
Scalar
Description
Lower Diagonal Element
of Solution Matrix (1-1)
Soil Horizon Drainage
Parameter
Advective Flux of Pesti-
cide
Adsorbed Portion of Pes-
ticide in Each Compart-
ment
Area of Field
Percolation Into Each
Soil Compartment
Maximum Rooting Depth
of Each Crop
Minimum Depth From Which
ET is Extracted Year
Sub-
routine Common
SLPEST PEST
TRDIAG
READ HYDR
ECHO
INITL
HYDR2
SLPEST PEST
MASBAL
OUTPST
OUTTSR
OUTCNC
READ HYDR
EROSN
HYDROL HYDR
HYDR1
HYDR2
READ CROP
INITL
PLGROW
READ CROP
INITL
I,M,0
O
I
O
I
I
I
O
I
I
I
O
O
I
I
O
I
I
O
I
ANUM
APD
APM
AVSTOR
AW
B
BD
cm
day-1
g cm"3
Around
Scalar Total Available Water
in Profile
Scalar Day of Month of Pesti-
cide Application
Scalar Month of Pesticide
Application
Scalar Available Water Storage
Scalar Fraction of Soil Voids
Occupied by Water
Array Diagonal Element of Solu-
tion Matrix (I)
Array Whole Soil or Mineral
Soil Bulk Density (Either
is Entered)
EVPOTR
READ
READ
HYDR2
EVPOTR
SLPEST
TRDIAG
READ
ECHO
INITL
THCALC
PESTAP
MASBAL
OUTPST
OUTTSR
OUTTSR
OUTCNC
PEST
HYDR
O
O
I
M
I
I
I
I
I
I
I
192
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable Units
BDFLAG
C day~1
CB kg ha~1
CEVAP cm
CFLAG
CINT cm
CINTB cm
CINTCP cm
CLAY percent
CN
CNDM
CNDMO
CONG
Type
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Array
Array
Array
Array
Alpha-
numeric
Description
Bulk Density Flag (0=
Whole Soil BD Entered,
1= Mineral BD and OC
Entered)
Upper Diagonal Element of
Solution Matrix (1+1)
Cumulative Pesticide
Balance Error
Current Daily Canopy
Evaporation Depth
Conversion Flag for
Initial Pesticide Input
Current Crop Interception
Storage
Crop Interception From
Previous Time Step
Maximum Interception
Storage of Each Crop
Percent CLay in the Soil
Horizon
Runoff Curve Numbers for
Antecedent Soil Moisture
Condition II
Accumulated Number of
Days in Each Month (With
and w/o Leap Year)
Accumulated Number of
Days in Each Month (With
and w/o Leap Year)
Flag for Output of Soil
Pesticide Concentration
Sub-
routine Common
READ
ECHO
INITL
SLPEST PEST
TRDIAG
OUTPST
EVPOTR HYDR
MASBAL
OUTHYD
OUTTSR
READ MISC
INITL
INITL HYDR
HYDRO!,
EVPOTR
MASBAL
OUTHYD
OUTTSR
PMAIN HYDR
MASBAL
OUTHYD
READ CROP
ECHO
PLGROW
READ HYDR
ECHO
INITL
THCALC
READ HYDR
ECHO
HYDROL
PMAIN
PMAIN MISC
READ
ECHO
INITL
OUTHYD
OUTPST
PMAIN
I,M,0
O
I
I
0
I
0
I
I
I
0
I
O
I
I
I
I
I
0
I
I
O
I
I
0
I
I
I
O
I
I
O
I
I
I
I
I
Profile
19]
-------
Table E--1 . PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
CONST
CORED
COVER
COVMAX
CPBAL
CURVN
CWBAL
DAY
DELT
DELX
DELXSQ
DENOM
Units Type
Scalar
cm Scalar
fraction Scalar
fraction Array
g cm"2 Scalar
Scalar
cm Scalar
Alpha-
numeric
day Scalar
cm Scalar
cm"2 Scalar
cm Scalar
Description
Constant Values Used to
Multiply Each Time Series
Output
Total Depth of Soil
Profile
Current Areal Cover of
Crop Canopy
Maximum Areal Coverage
of Each Crop at Full
Canopy Development
Cumulative Pesticide
Balance Error
Current Value of Runoff
Curve Number
Cummulative Water Balance
Error
Flag for Daily Output of
Water or Pesticide
Summary
Time Step
Compartment Thickness
Compartment Thickness
Squared
Total Voids in the Soil
Sub-
routine Common
READ
ECHO
OUTTSR
READ HYDP
ECHO
INITL
PLGROW CROP
PESTAP
OUTHYD
READ CROP
ECHO
INITL
PLGROW
MASBAL PEST
OUTPST
HYDROL
MASBAL HYDR
OUTHYD
PMAIN
INITL MISC
HYDR2
PLPEST
SLPEST
MASBAL
INITL HYDR
PLGROW
HYDROL
HYDR1
HYDR2
EROSN
PESTAP
SLPEST
MASBAL
OUTHYD
OUTPST
OUTTSR
INITL HKYDR
SLPEST
EVPOTR
I,M,0
O
I
I
0
I
I
O
I
T
O
I
I
I
M
I
M
I
O
I
I
I
I
O
I
I
I
I
I
I
I
I
I
I
I
0
DENOM
cm hr T Scalar
Profile
Available Water for Run-
off During a Storm
EROSN
194
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable Units
DEPI cm
DFFLUX g Cltr2
day-1
DIN cm
DISP cm2
day"1
DISS mg 1~1
DKFLUX g cm"2
DKRATE day"1
DOM
Dt hr
EF kg ha"1
ELTERM day~1
EMD
EMM
ENRICH
ERFLAG
ERFLUX g cm"2
Type
Array
Array
Scalar
Array
Array
Array
Array
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Depth of Pesticide
Incorporation
Diffusive/Dispersive
Flux of Pesticide Leaving
Each Soil Compartment
Current Plant Canopy
Interception Potential
Dispersion/Diffusion
Coefficient
Dissolved Portion of
Pesticide in Each
Compartment
Decay Flux of Pesticide
From Each Compartment
Pesticide Decay Rate in
Each Soil Horizon
Number of Current Day of
Month of Simulation
Average Hours of Daylight
for a Day falling in Each
Month
Daily Erosion Flux
Erosion Loss Term for
Pesticide Balance
Day of Month of Crop
Emergence
Month of Crop Emergence
Enrichment Ratio for
Organic Matter
Erosion Flag (0= Not
Calculated 1= Calculated)
Erosion Flux of Pesticide
From Soil Surface
Sub-
routine Common
READ PEST
ECHO
PESTAP
SLPEST PEST
OUTPST
OUTTSR
PLGROW HYDR
HYDROL
OUTHYD
READ PEST
ECHO
INITL
SLPEST
OUTCNC
SLPEST PEST
MASBAL
OUTPST
OUTTSR
READ PEST
ECHO
INITL
SLPEST
PMAIN MISC
OUTHYD
OUTPST
OUTTSR
OUTCNC
READ MET
ECHO
EVPOTR
OUTPST
EROSN PEST
SLPEST
READ
ECHO
READ
ECHO
EROSN
READ HYDR
PMAIN
SLPEST PEST
MASBAL
OUTPST
I,M,0
O
I
0
I
I
O
I
I
O
I
I
I
O
I
I
I
O
I
I
I
O
I
I
I
I
0
I
I
0
I
0
I
0
I
I
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
EXTRA
F
F0
FAM
FC
FCV
FDAY
FEXTRC
FILTRA
FL
FOLP0
FP
FPDLOS
FPWLOS
Uni ts Type
cm3 cm~3 Scalar
g cm"2 Array
day~1
kg ha~1 Scalar
- Scalar
cm Array
Array
Scalar
cm"1 Scalar
m2 kg"1 Scalar
kg ha~~1 Scalar
g cm"2 Scalar
kg ha~1 Scalar
g cm~2 Scalar
g cm"2 Scalar
Description
Extra Water Occurring in
a Compartment Over the
Allowed Saturation Amount
Vector of Source Terms
for Each Compartment
(Tri-diagonal Matrix)
Current Foliar Pesticide
Storage
Pesticide Application
Flag (1= Soil, 2= Linear
Foliar, 3= Exponential
Foliar)
Field Capacity Water
Depth in Soil Compartment
Regression Coefficients
for Prediction of Field
Capacity Soil Water
Content
Loop Limit, First Day
Foliar Extraction Coef-
ficient for Foliar Wash-
off Model
Filtration Parameter for
Exponential Foliar Appli-
cation Model
Foliar Pesticide Decay
Loss
Foliar Pesticide Storage
From Previous Time Step
Current Daily Foliar
Pesticide Storage
Current Daily Foliar
Pesticide Decay Loss
Current Daily Pesticide
Sub-
routine Common
OUTTSR
HYDR2
SLPEST PEST
TRDIAG
OUTPST
READ PEST
ECHO
PESTAP
INITL HYDR
EVPOTR
THCALLC
PMAIN
READ PEST
ECHO
PLPEST
READ PEST
ECHO
PESTAP
OUTPST
PLPEST PEST
MASBAL
OUTPST
PMAIN
OUTPST
PLPEST PEST
MASBAL
OUTPST
OUTTSR
PLPEST
I,M,0
I
O
I
O
I
I
O
O
I
I
O
I
I
O
I
I
I
O
I
I
I
FRAC
FRAC
Washoff Loss
Scalar Fraction of the Distance READ
a Curve Number is Between
Increments of Ten
Scalar Fraction of the Current PLGROW
Crop Growing Season
Completed
196
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable Units
FRAC
FRACOM
GAMMA
HAD
HAM
HORIZN
HSWZT
HTITLE
I
IAPDY
IAPYR
IARG
IARG1
IB
IBM1
ICNAH
Type
Array
Scalar
Array
Scalar
Scalar
Array
Scalar
Alpha-
numeric
Scalar
Array
Array
Array
Scalar
Scalar
Scalar
Array
Description
Number of Compartments
Available to Extraction
of ET
Fraction of Layer Attri-
buted to the Current
Horizon
Pesticide Uptake Effic-
iency by Plant
Day of Month of Crop
Harvest
Month of Crop Harvest
Soil Horizon Number
Hydraulics Flag (0= Free
Draining Soils, 1= Res-
tricted Drainage)
Comment Line to Enter
Information about Hydro-
logy Parameters
Loop Counter
Julian Day of Pesticide
Application
Year of Pesticide
Application
Argument of Variable
Identified by 'PLNAME'
Argument of Variable
Identified by 'PLNAME1
Backward Loop Index
Counter
Soil Surface Condition
After Harvest
Sub-
routine
EVPOTR
INITL
PLGROW
SLPEST
READ
ECHO
READ
ECHO
READ
ECHO
INITL
OUTHYD
OUTPST
OUTCNC
READ
ECHO
INITL
PMAIN
READ
ECHO
ALL SUP-
ROUTINES
READ
ECHO
PMAIN
READ
ECHO
PMAIN
READ
ECHO
OUTTSR
OUTTSR
INITL
HYDR2
INITL
READ
ECHO
PLGROW
Common I,M,O
PEST O
I
MISC O
I
I
I
I
I
O
I
I
I
MISC O
I
I
MISC 0
I
I
MISC O
I
I
HYDR O
I
I
197
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable Units
ICNCN
IEDAY
IEDY
IEMER
IEMON
I ERROR
Type
Array
Scalar
Scalar
Array
Scalar
Scalar
Description
Crop Number
Ending Day of Simulation
Counter
Julian Day of Crop
Emergence
Ending Month of Simula-
tion
Error Flag if Tri-
Diagonal Matrix Cannot
Sub-
routine
READ
ECHO
INITL
PLGROW
READ
PMAIN
ECHO
INITL
READ
ECHO
INITL
PLGROW
READ
ECHO
PMAIN
SLPEST
TRDIAG
Common I,M,O
CROP O
I
I
I
MISC 0
I
I
CROP O
I
I
I
MISC O
I
I
IEYR
IFIRST
IHAR
II
IJ
ILP
INABS cm
INCROP
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Array
be Saved
Ending Year of Simulation READ
ECHO
PMAIN
Flag to Print Output OUTTSR
Heading and Initialize
Output Array
Julian Day of Crop
Harvest
Loop Counter
Loop Counter
Initial Level of Pesti-
cide Flag (0= No Pesti-
READ
ECHO
INITL
PLGROW
OUTPST
PMAIN
READ
ECHO
cide, 1= Initial Pesticide)
Initial Abstraction of
Water from Potential
Surface Runoff
Crop Growing in Current
Cropping Period
INICRP
Scalar
Initial Crop Number if
Simulation starting Date
is Before First Crop
Emergence Date
HYDROL
EROSN
READ
ECHO
INITL
PLGROW
OUTHYD
OUTPST
READ
ECHO
INITL
MISC
CROP
MISC
HYDR
CROP
CROP
O
I
I
O
I
I
I
0
I
O
I
O
I
I
I
I
O
I
I
198
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
INTFC
IPEIND
ISCOND
ISDAY
ISDY
ISMON
ISTYR
ITEM1
ITEM2
ITEMS
I TEMP
ITMP
IY
Uni ts Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Alpha-
numeric
Alpha-
numeric
Alpha-
numeric
degree C Scalar
Scalar
-
Description
Whole Layer(s) Attributed
to the Current Horizon
Pan Evaporation Indica-
tor Flag (0= Data Read
In, 1= Calculated)
Surface Condition After
Harvest Corresponding to
'INICRP'
Starting Day of Simula-
tion
Counter
Starting Month of Simu-
lation
Starting Year of Simula-
tion
Hydrology Output Summary
Indicator
Pesticide Output Summary
Indicator
Soil Pesticide Concentra-
tion Profile Output
Indicator
Mean Daily Temperature
Rounded to Next Lowest
Whole Number
Number of Compartments
Pesticide is Applied to
When Incorporated
Annual Loop Counter
Sub-
routine
INITL
READ
ECHO
READ
ECHO
PLGROW
HYDROL
EROSN
READ
ECHO
INITL
PMAIN
INITL
READ
ECHO
INITL
PMAIN
READ
ECHO
INITL
PMAIN
READ
ECHO
OUTHYD
READ
ECHO
OUTPST
READ
ECHO
PMAIN
EVPOTR
PESTAP
PMAIN
PLGROW
OUTHYD
OUTPST
OUTTSR
OUTCNC
Common I,M,O
MET O
I
HYDR 0
I
I
I
I
MISC O
I
I
I
MISC 0
I
I
I
MISC 0
I
I
I
MISC O
I
I
MISC O
I
I
MISC 0
I
I
MISC O
I
I
I
I
I
I
199
-------
Table E-1. PRZM Program Variables, units, Location, and
Variable Designation (Continued)
Variable
IYREM
IYRHAR
IYRMAT
J
JJ
JP1
JP1T10
JT1 0
JULDAY
K
KD
KDFLAG
KK
KOC
L
Uni ts Type
Array
Array
- Array
Scalar
Scalar
- Scalar
Scalar
Scalar
Scalar
Scalar
cm3 g ~1 Array
Scalar
Scalar
cm-^ g~1 Scalar
-oc
- Scalar
Description
Year of Crop Emergence
Year of Crop Harvest
Year of Crop Maturation
Loop Counter
Loop Counter
Counter (J+1)
Counter (JP1*10)
Counter (J*10)
Julian Day
Loop Counter
Adsorption/Partition
Coefficient for Soil
Compartment
Partition Coefficient
Flag (0= Kd Read In,
1= Kd Calculated)
Loop Counter
Organic Carbon Partition
Coefficient
Lower Decomposed Matrix
Sub-
routine
READ
ECHO
INITL
PLGROW
READ
ECHO
INITL
PLGROW
READ
ECHO
INITL
PLGROW
PMAIN
READ
ECHO
INITL
PLGROW
OUTHYD
OUTPST
READ
READ
READ
READ
PMAIN
PLGROW
OUTHYD
OUTPST
READ
ECHO
READ
ECHO
INITL
KDCALC
PESTAP
SLPEST
MASBAL
OUTPST
OUTTSR
OUTCNC
READ
ECHO
PMAIN
READ
KDCALC
TRDIAG
Common I,M,O
CROP O
I
I
I
CROP O
I
I
I
CROP O
I
I
I
MISC 0
I
I
I
PEST O
I
I
0
I
I
I
I
I
I
0
I
I
200
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable Units
LDAY
LEAP
LFREQ1
LFREQ2
LFREQ3
LOGKOC
MAD
MAM
MAT
MD
MDOUT kg ha~1
MEOUTW cm
MINPP kg ha~1
MINPP1 kg ha~1
MINPP2 kg ha~1
MINPW cm
MINPW1 cm
MINPW2 cm
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Array
Array
Array
Scalar
Scalar
Array
Scalar
Scalar
Description
Loop Limit (Last Day)
Additional Day Flag for
Leap Year
Frequency of Soil Com-
partment Reporting in
Water Output Summary
Frequency of Soil Com-
partment Reporting in
Pesticide Output Summary
Frequency of Soil Com-
partment Reporting in
Concentration Profile
Output Summary
Natural Log of Koc
Day of Month of Crop
Maturation
Month of Crop Maturation
Julian Day of Crop
Maturation
Number of Day Read from
Meteorologic File
Monthly Pesticide Decay
from Each Compartment
Monthly ET from Each Soil
Compartment
Monthly Advection/Disper-
sion Flux from Each
Compartment
Monthly Foliar Applied
Pesticide
Monthly Soil Applied
Pesticide
Monthly Infiltration into
Each Soil Compartment
Monthly Precipitation
Monthly Snowfall
Sub-
routine
PMAIN
READ
ECHO
INITL
PMAIN
OUTHYD
OUTPST
READ
OUTHYD
READ
OUTPST
READ
OUTCNC
KDCALC
READ
ECHO
READ
ECHO
READ
ECHO
INITL
PLGROW
PMAIN
OUTPST
OUTHYD
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
Common
MISC
MISC
MISC
MISC
MISC
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I,M,0
o
I
I
I
I
I
o
I
0
I
0
I
0
I
I
I
M
M
M
M
M
M
M
M
201
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
MM
MI NTH
MNTHP1
MODFC
MONTH
MOUTP
MOUTP1
MOUTP2
MOUTP3
MOUTP 4
MOUTP5
MOUTP6
MOUTW
MOUTW1
MOUTW2
MOUTW3
MOUTW4
MOUTW5
MOUTW6
MSTART
MSTR
MSTR1
Units
-
-
-
-
kg ha"1
kg ha~1
kg ha"1
kg ha~1
kg ha~1
kg ha"1
kg ha~1
cm
cm
cm
cm
cm
cm
MTonne
-
cm
cm
Type
Scalar
Alpha-
numeric
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Description
Number of Month Read from
Meteorologic File
Flag for Monthly Output
Summary (for Either Water
or Pesticide)
Current Month Plus 1
(Month + 1 )
Fraction Multiplier
Number of Current Month
of Simulation
Monthly Pesticide Uptake
from Each Compartment
Monthly Pesticide Washoff
Flux
Monthly Pesticide Runoff
Flux
Monthly Pesticide Erosion
Flux
Monthly Foliar Pesticide
Decay Loss
Monthly Pesticide Uptake
Flux from Profile
Monthly Pesticide Decay
Flux from Profile
Monthly Exfiltration from
Each Compartment
Monthly Canopy Evapo-
ration
Monthly Thrufall
Monthly Runoff
Monthly Snowmelt
Monthly Evapotrans-
piration
Total Monthly Sediment
Loss
Flag for Positioning
Meteorologic File
Previous Month Storage
of Water in Each Soil
Compartment
Monthly Canopy Inter-
ception
Sub-
routine
PMAIN
PMAIN
OUTHYD
INITL
PMAIN
EVPOTR
OUTHYD
OUTPST
OUTTSR
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
PMAIN
OUTHYD
OUTHYD
Common
MISC
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I,M,0
0
I
I
I
I
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
202
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable Units
Type
Description
Sub-
routine
Common I,M,0
MSTR2
MSTRP
MSTRP1
MY
N
NAP PC
NAPS
NBYR
NCOM0
NCOM1
NCOM2
cm Scalar
kg ha~1 Array
kg ha~1 Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
NCOM2M
NCOMRZ
Scalar
Scalar
Monthly Accumulation of
Snow
Storage of Pesticide from
Previous Month in Each
Soil Compartment
Storage of Foliar Pesti-
cide from Previous Month
Number of Year Read from
Meteorologic File
Number of Compartments in
the Soil Profile
Pesticide Application
Counter
Number of Pesticide
Applications in the
Simulation
Beginning Year of Crop
Growth for Current Crop
(Loop Limit)
Number of Compartments
from Which ET is Ex-
tracted Year Round
Current Number of Com-
partment that ET is
Extracted From
Number of Compartments
in Soil Profile
OUTHYD
OUTPST
OUTPST
PMAIN
TRDIAG
Number of Compartments
in Soil Profile Minus 1
(NCOM2 = 1)
Number of Compartments
in the Root Zone
PLGROW
EVPOTR
OUTHYD
READ
ECHO
INITL
PMAIN
EVPOTR
HYDR1
HYDR2
SLPEST
MASBAL
OUTHYD
OUTPST
OUTCNC
TNITL
SLPEST
INITL
SLPEST
OUTHYD
OUTPST
ACCUM M
ACCUM M
ACCUM M
PMAIN PEST
PESTAP
READ PEST
ECHO
INITL
PMAIN
INITL
PLGROW
INITL HYDR
PLGROW
0
I
0
I
I
I
0
I
HYDR
HYDR
HYDR
CROP
O
I
I
0
I
I
I
I
I
I
I
I
I
I
I
O
I
O
I
I
I
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable Units
NCP
NCPDS
NCROP
NDC
NDCNT
NDYRS
NEXDAY
NEYR
NHORIZ
NLINES
NM1
NOPRT
NPLOTS
NRZCOM
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Number of Current Crop-
ping Period
Number of Cropping
Periods in the Simulation
Number of Current Crop
Number of Different Crops
in Simulation
Number of Days Since Crop
Emergence for Current
Crop
Number of Years Between
Emergence and Maturation
of a Crop
Extra Day Added for Leap
Year
Ending Year of Crop
Growth for Current Crop
Total Number of Soil
Horizons
Numbers of Lines for
Listing Initial Pesticide
in Profile (Loop Limit)
Number of Compartments
in Profile Minus 1
(NCOM2 - 1 )
Print Flag
Number of Time Series to
be Output (Maximum of 7)
Current Number of Layers
Sub-
routine
INITL
PLGROW
READ
ECHO
INITL
PLGROW
INITL
PLGROW
HYDROL
EROSN
READ
ECHO
INITL
PLGROW
INITL
PLGROW
INITL
PLGROW
PLGROW
INITL
PLGROW
READ
ECHO
INITL
KDCALC
ECHO
TRDIAG
OUTHYD
OUTPST
READ
ECHO
PMAIN
OUTTSR
PLGROW
Common I , M , O
CROP O
I
CROP O
I
I
I
CROP O
I
I
I
CROP O
I
I
I
MISC O
I
MISC O
I
I
I
MISC O
I
I
I
NSUM
in Root Zone
Scalar Cummulative Sum of Com-
partment Numbers
EVPOTR
204
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
NSUMM
OC
ORGM
OSNOW
OUTPUT
PA
PB
PBAL
PCMC
PESTR
PET
PETP
PEVP
PFAC
PLDKRT
PLNAME
Uni ts Type
Scalar
percent Array
percent Scalar
cm Scalar
Array
kg ha~1 Scalar
kg ha~1 Scalar
g cm~2 Scalar
Scalar
g cm~3 Array
cm Scalar
cm Scalar
cm Scalar
- Scalar
day"1 Array
Alpha-
numeric
Description
Termination Loop Index
for Summary Output
Organic Carbon Content
of Each Soil Horizon
Organic Matter Content
of a Soil Horizon
Snow Accumulated at the
End of the Previous Time
Step
Output Array for Time
Series
Daily Foliar Pesticide
Application
Pesticide Balance
Current Pesticide Balance
Error
Partition Coefficient
Model Flag (1= Karick-
hoff, 2= Kenega,
3= Chiou)
Total Pesticide in Each
Soil Compartment
Total Daily Potential
Evapo transpiration
Running Total of Avail-
able Evapotranspiration
Pan Evaporation
Pan Factor for ET
Foliar Pesticide Decay
Rate
Time Series Output Iden-
tifier (Options Listed
in User's Guide)
Sub-
routine
OUTHYD
OUTPST
READ
ECHO
INITL
THCALC
KDCALC
INITL
PMAIN
HYDROL
MASBAL
OUTTSR
OUTPST
OUTPST
MASBAL
OUTPST
READ
KDCALC
READ
ECHO
INITL
PMAIN
PESTAP
MASBAL
OUTPST
EVPOTR
EVPOTR
PMAIN
EVPOTR
READ
ECHO
EVPOTR
READ
ECHO
PLPEST
READ
OUTTSR
Common I,M,O
PEST O
I
I
I
I
HYDR O
I
I
PEST O
MISC O
I
PEST O
I
I
I
I
I
I
MET O
I
MET O
I
I
PEST O
I
I
MISC O
I
205
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
PLNTAP
PNBRN
PRECIP
PTITLE
Q
QQP
RF
RMULT
RMULT1
RMULT3
RNSUM
RNUM
RODPTH
ROFLUX
RUNOF
RZD
Uni ts Type
g cm"2 Scalar
Array
cm Scalar
- Alpha-
numeric
m-* Scalar
m^ sec"^ Scalar
kg ha"1 Scalar
- Scalar
- Scalar
- Scalar
Sealer
ha cm"2 Scalar
- Scalar
g cm"2 Scalar
day"1
cm Scalar
cm Scalar
Description
Pesticide Applied to Crop
Canopy
Output Array for Time
Series
Precipitation
Comment Line to Input
Information About Pesti-
cide Parameters
Runoff Volume
Runoff Energy Factor
Pesticide Runoff Flux
Multiplication Factor for
Time Series Output
Multiplication Factor for
Curve Number AMC I
Multiplication Factor for
Curve Number AMC III
Converts NSUM to a Real
Number
Numerator of Peak Runoff
Rate
Number of Soil Compart-
ments that Affect Runoff
Runoff Flux of Pesticide
From Land Surface
Current Runoff Depth
Maximum Root Zone Depth
for All Crops
Sub-
routine
PESTAP
OUTPST
OUTTSR
OUTTSR
PMAIN
HYDROL
EROSN
MASBAL
OUTHYD
OUTTSR
READ
ECHO
EROSN
EROSN
OUTPST
OUTTSR
READ
READ
EVPOTR
EROSN
HYDROL
SLPEST
MASBAL
OUTHYD
OUTTSR
HYDROL
PMAIN
EROSN
SLPEST
MASBAL
OUTHYD
OUTTSR
INITL
OUTHYD
Common I , M , O
PEST O
I
I
MET O
I
I
I
I
I
MISC O
I
PEST O
I
I
I
HYDR O
I
I
I
I
I
I
206
-------
Table E-1. PRZM program Variables, Units, Location, and
Variable Designation (Continued)
Variable
RZFLUX
RZI
SA
SAND
SD
SDKFLX
SEDL
SFAC
SJDAY
SLKGHA
SMELT
SNOW
SNOWFL
SO I LAP
Units
g cm" ^
-
kg ha~1
percent
kg ha~1
g cm" ^
day~1
MTonne
day"1
cm
degree
c-1
-
kg ha~1
day~1
cm
cm
cm
g cm~2
Type Description
Scalar Dispersive/Advective Flux
of Pesticide Past the
Bottom Root Zone Com-
partment
Scalar Active Root Zone Flag
Scalar Application of Pesticide
to the Soil
Array Percent Sand in Each Soil
Horizon
Scalar Sum of the Decay Fluxes
From All Compartments
in Soil Profile
Scalar Sum of the Decay Fluxes
From All Compartments in
Soil Profile
Scalar Erosion Sediment Loss
Scalar Snowmelt Factor
Scalar Starting Day of Simula-
tion
Scalar Erosion Sediment Loss
Scalar Current Daily Snowmelt
Depth
Scalar Snowpack Accumulation
Depth
Scalar Current Snowfall Depth
Array Pesticide Applied to the
Soil
Sub-
routine
SLPEST
OUTTSR
INITL
PLGROW
OUTPST
READ
ECHO
INITL
THCALC
OUTPST
SLPEST
OUTPST
PMAIN
EROSN
OUTHYD
READ
ECHO
HYDROL
EROSN
HYDROL
EROSN
OUTHYD
INITL
PMAIN
HYDROL
MASBAL
OUTHYD
OUTTSR
HYDROL
MASBAL
OUTHYD
OUTTSR
PESTAP
PMAIN
OUTPST
OUTTSR
Common I , M , O
PEST O
I
MISC O
I
HYDR O
I
I
I
PEST O
I
HYDR O
M
O
MET O
I
I
INITL
HYDR O
HYDR O
I
I
I
I
I
MET O
I
I
I
PEST 0
I
I
I
207
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
SOL
SPESTR
STEP1
STEP 2
STEP3
STITLE
SU
SUMXP
SUPFLX
SW
T
TAPP
TEMP
Uni ts Type
mole Scalar
fraction
mg I-1
umoles 1~1
g cm" ^ Array
Alpha-
numeric
Alpha-
numeric
- Alpha-
numeric
- Alpha-
numeric
kg ha"^ Scalar
kg ha~1 Scalar
g cm"~2 Scalar
day-"1
cm Array
Sealer
g cm~ 2 Array
degree C Scalar
Description
Pesticide Solubility -
Karickhoff Model
Kenaga Model
Chiou Model
Dissolved Pesticide in
Each Soil Compartment
Time Step of Water Output
Summary
Time Step of Pesticide
Output Summary
Time Step of Concentra-
tion Profile Output
Summary
Comment Line to Input
Information About Soil
Parameters
Sum of the Uptake Fluxes
From All Soil Compart-
ments
Sum of Soluble Pesticide
in Profile
Sum of the Uptake Fluxes
From All Soil Compart-
ments
Current Water Depth in
Each Soil Compartment
Fraction Compartment
Check
Total Pesticide Applied
Per Application
Ambient Air Temperature
Sub-
routine
READ
KDCALC
INITL
PMAIN
PESTAP
SLPEST
READ
ECHO
OUTHYD
READ
ECHO
OUTPST
READ
ECHO
OUTCNC
READ
ECHO
OUTPST
OUTPST
SLPEST
OUTPST
OUTTSR
INITL
HYDROL
EVPOTR
HYDR1
HYDR2
SLPEST
OUTTSR
INITL
READ
ECHO
INITL
PESTAP
PMAIN
HYDROL
EVPOTR
Common I , M , O
O
I
PEST O
I
I
I
MISC 0
I
I
MISC O
I
I
MISC O
I
I
MISC O
I
PEST O
I
I
HYDR O
I
I
I
I
I
I
PEST O
I
I
I
MET O
I
I
208
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
TERM
TERM1
TERM2
TFRAC
THEFC
THETAS
THETH
THETN
THETO
THEWP
Units Type Description
Scalar Exponential Pesticide
Washoff Term
Scalar Exponential Pesticide
Decay Term
- Scalar Product of Washoff and
Decay Terms
Sealer Total Fraction of Com-
partments Available for
Evapotranspiration Ex-
traction
cm3 cm"3 Array Field Capacity Water Con-
tent for Each Soil
Horizon
cm3 cm~3 Array Soil Compartment Water
Content at Saturation
cm3 cm"3 Scalar Soil Moisture Content
Half Way Between Wilting
Point and Field Capacity
in the Top Soil Com-
partments
cm3 cm"3 Array Soil Water Content at the
End of the Current Day
for Each Soil Compartment
cm3 cm"3 Array Soil Water Content at the
End of the Previous Day
for Each Soil Compartment
cm3 cm"3 Array Wilting Point Water
Content for Each Soil
Horizon
Sub-
routine
PLPEST
PLPEST
PLPEST
EVOPTR
READ
ECHO
INITL
THCALC
HYDR1
HYUR2
INITL
HYDR2
INITL
HYDROL
HYDR1
HYDR2
PMAIN
SLPEST
MASBAL
OUTHYD
OUTPST
OUTTSR
OUTCNC
READ
HYDR1
HYDR2
PESTAP
SLPEST
MASBAL
OUTHYD
OUTPST
READ
ECHO
INITL
THCALC
Common I,M,0
HYDR O
I
I
O
I
I
HYDR O
I
HYDR 0
I
HYDR O
O
I
I
I
I
I
I
I
HYDR 0
M
M
I
I
I
I
I
HYDR 0
I
I
0
209
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
THFLAG
THKNS
THRUFL
TITLE
TNDGS
TOL
TOTAL
TR
TS
Uni ts Type
Scalar
cm Array
cm Scalar
Alpha-
numeric
day Array
Sealer
mg kg~^ Array
hr Scalar
cm^ cm~3 Array
Description
Soil Water Content Flag
(0= Field Capacity and
Wilting Point are Input,
1= Field Capacity and
Wilting Point are Calcu-
lated)
Soil Horizon Thickness
Precipitation that Falls
Past the Crop Canopy to
the Soil Surface
Title of the Simulation
(User Supplied)
Total Number of Days in
Each Growing Season
Fraction Compartment
Check
Total Pesticide in Each
Compartment
Duration of Average Ero-
sive Storm Event
Previous Soil Compartment
Sub-
routine
READ
ECHO
PMAIN
READ
ECHO
INITL
HYDROL
HYDROL
OUTHYD
OUTTSR
READ
ECHO
INITL
PLGROW
INITL
OUTCNC
READ
ECHO
EROSN
HYDR2
Common I , M , O
MISC O
I
I
MISC 0
I
I
I
MET O
I
I
MISC 0
I
CROP O
I
MET O
I
I
TSW
TTHKNS
TWLVL
TWP
U
UPF
cm
cm
Sealer
Scalar
cm cm~^ Scalar
cm
,-1
Sealer
Array
kg ha ' Scalar
Water Content Minus
Evapotranspiration
Total Soil Water in Com- EVOPTR
partments Available for
Evapotranspiration Ex-
traction
Total Thickness of Soil INITL
Profile (For Computa-
tional Check)
Fraction of Water to Soil HYDROL
Depth for Runoff Calcu-
lation
Total Wilting Point EVOPTR
Depth in Compartments
Available for Evapo-
transpiration Extraction
Upper Decomposed Matrix TRDIAG
Daily Pesticide Uptake OUTPST
Flux in Profile
210
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable Units
UPFLUX g cm- 2
UPTKF
USLEC
USLEK
USLELS
USLEP
VAR1 kg ha~1
Type
Array
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Description
Uptake Flux of Pesticide
From Each Soil Compart-
ment
Plant Pesticide Uptake
Efficiency Factor
Universal Soil Loss
Equation 'C' Factor
Universal Soil Loss
Equation 'K1 Factor
Universal Soil Loss
Equation ' Ls ' Factor
Universal Soil Loss
Equation 'P' Factor
Daily Advection/Disper-
Sub-
routine
SLPEST
OUTPST
READ
ECHO
PLGROW
READ
ECHO
EROSN
READ
ECHO
EROSN
READ
ECHO
EROSN
READ
ECHO
EROSN
OUTPST
Common I,M,O
PEST O
I
PEST O
I
I
HYDR 0
I
I
HYDR O
I
I
HYDR O
I
I
HYDR O
I
I
VAR2
VAR2D
VAR2M
VAR2RZ
VAR2Y
VAR3
VEL
WBAL
kg ha ^ Scalar
cm
cm
Scalar
Scalar
kg ha~1 Scalar
cm
Scalar
kg ha~1 Scalar
cm
cm
Array
Scalar
sion Flux of Pesticide
Into a Compartment
Daily Advection/Disper- OUTPST
sion Flux of Pesticide
Out of a Compartment
Water Storage in a Single OUTHYD
Compartment for the
Previous Day
Water Storage in a Single OUTHYD
Compartment for the
Previous Month
Daily Advection/Disper- OUTPST
sion Flux of Pesticide
Out of the Root Zone
Water Storage in a Singel OUTHYD
Compartment for the
Previous Year
Pesticide Storage in a OUTPST
Single Compartment for
the Previous Day
Water Velocity in Each
Soil Compartment
Current Water Balance
Error
HYDR1
HYDR2
SLPEST
MASBAL
OUTHYD
HYDR
HYDR
O
0
I
0
I
211
-------
Table E-1. PRZM Program Variables, Units, Location, and
Variable Designation (Continued)
Variable
WEIGHT
WF
WFMAX
WLVL
WOFLUX
WP
WPV
WTERM
X
XP
Y
YDOUT
YEAR
YEOUTW
YINPP
YINPP1
Units
kg m"2
kg ha~1
kg m"2
cm
g cm"2
day-1
cm
-
g cm"2
g cm"3
g cm"3
-
kg ha~1
-
cm
kg ha~1
kg ha"1
Type
Scalar
Scalar
Array
Scalar
Scalar
Array
Array
Scalar
Array
Array
Array
Array
Alpha-
numeric
Array
Array
Scalar
Description
Current Plant Dry Foliage
Weight
Daily Pesticide Washoff
Flux
Maximum Plant Dry Foliage
Weight at Full Canopy
Total Soil Water in the
Compartments that Affect
Runoff
Washoff Flux of Pesticide
From Plant Foliage
Wilting Point Water Depth
in a Soil Compartment
Regression Coefficients
for Prediction of wilt-
ing Point Soil Water
Content
Current Daily Pesticide
Washoff Loss
Dissolved Pesticide in
Each Soil Compartment
Total Pesticide in
Each Soil Compartment
Intermediate Matrix Solu-
tion Array
Annual Pesticide Decay
From Each Soil Compart-
ment
Flag for Annual Water and
Pesticide Summary Output
Annual Evapotranspiration
From Each Soil Compart-
ment
Annual Advective/Disper-
sive Flux Into Each Soil
Compartment
Annual Pesticide Applied
to Foliage
Sub-
routine
PLGROW
PESTAP
OUTPST
READ
ECHO
INITL
HYDRO L
SLPEST
OUTPST
EVPOTR
THCALC
PLPEST
SLPEST
TRDIAG
SLPEST
MASBAL
OUTPST
OUTTSR
OUTCNC
PMAIN
MASBAL
TRDIAG
OUTPST
PMAIN
OUTHYD
OUTPST
OUTPST
Common I,M,O
CROP O
I
CROP O
I
I
PEST O
I
HYDR O
PEST 0
I
PEST
PEST O
I
I
I
I
I
ACCUM M
ACCUM M
ACCUM M
ACCUM M
212
-------
Table E-1. PRZM program Variables, Units, Location, and
Variable Designation (Continued)
Variable
YINPP2
YINPW
YINPW1
YINPW2
YOUTP
YOUTP1
YOUTP2
YOUTP3
YOUTP4
YOUTP5
YOUTP 6
YOUTW
YOUTW1
YOUTW2
YOUTW3
YOUTW 4
YOUTW5
YOUTW6
YSTR
YSTR1
YSTR2
YSTRP
YSTRP1
Units
kg ha~~1
cm
cm
cm
kg ha-1
kg ha~1
kg ha~1
kg ha~1
kg ha~1
kg ha~1
kg ha~1
cm
cm
cm
cm
cm
cm
MTonne
cm
cm
cm
kg ha~1
kg ha~1
Type
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Description
Annual Pesticide Applied
to Soil
Annual Infiltration Into
Each Soil Compartment
Annual Precipitation
Annual Snowfall
Annual Pesticide Uptake
From Each Soil Compart-
ment
Annual Pesticide Washoff
Flux
Annual Pesticide Runoff
Flux
Annual Pesticide Erosion
Flux
Annual Foliar Pesticide
Decay Flux
Total Annual Pesticide
Uptake Flux
Total Annual Pesticide
Soil Decay Flux
Annual Exf il tration From
Compartment
Annual Canopy Evaporation
Annual Tiufall
Annual Runoff
Annual Snowmelt
Total Annual Evapotrans-
piration
Total Annual Sediment
Loss
Previous Year Storage of
Water in Each Soil Com-
partment
Annual Canopy Intercep-
tion
Annual Snow Accumulation
Storage of Pesticide From
Previous Year in Each
Soil Compartment
Storage of Foliar Pesti-
cide
Sub-
routine
OUTPST
OUTHYD
OUTHYD
OUTHYD
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTPST
OUTPST
Common
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
AC C Urn
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUm
ACCUM
ACCUM
ACCUM
ACCUM
I,M,0
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
-------
ters will specify logical units for the meteorological, parameter input,
water summary output, pesticide summary output, time series output, and
pesticide concentration profile output files.
SEGMENTATION
Model overlays or segmentation may be required to fit PRZM on smaller
computer systems. To determine whether segmentation is required, the
program should be compiled and a link (or load) attempted. If the link
fails due to lack of memory, segmentation is required. Segmentation allows
the various PRZM subroutines to share the same memory locations.
There are two factors that will impact the method of segmentation;
execution time and memory availability. Segmentation of any or all of the
first five subroutines (READ, ECHO, THCALC, KDCALC, INITL) has very little
impact on run-time as these subroutines are accessed only once, at the
beginning of the simulation. To reduce the size of PRZM significantly, the
largest of the subroutines should be segmented. These include READ, ECHO,
OUTHYD, and OUTPST. Sizes of subroutines are generally output by the
FORTRAN compiler.
It may be possible to avoid segmentation by reducing the size of the
arrays found in the first PARAMETER statement. To help compute space
savings, there are 34 arrays dimensioned by NCMPTS, 16 arrays dimensioned
by NC, and 4 arrays dimensioned by NAPP in PRZM.
BLOCK DATA SUBPROGRAM
Accompanying the PRZM code is a Block Data subprogram used to initia-
lize all parameters, state variables, and fluxes found in the COMMON blocks,
This file is divided into two sections with the first devoted to input
parameters and the second to all other variables in common. The input
parameters are set equal to -999. or their default value to facilitate
input checking. All other variables are set to zero. This is included for
those machines that do not zero all memory space on their own.
OUTPUT FILES
As noted in the discussion of parameter statements, there are six
input/output files that are used or can be generated by PRZM. Four of
these are output files.
The hydrology (or water) output file contains an echo of the user in-
puts from the input file followed by daily, monthly and/or annual hydrology
summaries. The output file must have a record length of 132 characters to
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accommodate this information. The echoing of input variables usually re-
quires this information. The echoing of input variables usually requires
no more than 120 records although longer simulation runs may require more.
Each of the hydrology summaries (daily, monthly or annual) require at a
minimum 55 records (assuming only two soil compartments are printed) and
this number increases by one record for each additional soil compartment
result printed.
Each pesticide summary output requires a record length of 132 charac-
ters with a minimum of 50 records of output. An additional record is added
for each soil compartment result exceeding two printed out.
The pesticide concentration output requires a minimum of 12 records
which must be of 50-character length. Again, each soil compartment result
printed per summary requires an additional record.
FATAL ERROR MESSAGES
There are several locations in the PRZM code where the programs may
terminate with a printed error message in the hydrology output file. Two
of these locations are in the MAIN program, six are in SUBROUTINE READ and
two are in SUBROUTINE INITL. All of these messages occur because of errors
occurring in the input parameter data set. The error messages and the
appropriate user responses are shown in Table E-2.
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Table E-2. PRZM Fatal Error Messages and Appropriate User Actions
SUBROUTINE
ERROR MESSAGE
USER ACTION
MAIN
READ
FORMAT ERROR IN THE INPUT SEQUENCE
RECHECK INPUT FILE
END OF INPUT FILE FOUND TOO SOON
RECHECK INPUT SEQUENCE
NDC VALUE OF 'X1
NC VALUE OF 'Y'
IS GREATER THAN
Check for format errors
in input sequence
(e.g., real numbers in
integer fields, etc.).
Check for missing lines
in input sequence.
Reduce value of NDC in
input or increase value
of NC in parameter state-
ment.
NCPDS VALUE OF 'X1
NC VALUE OF 'Y1
IS GREATER THAN
Reduce value of NCPDS in
input or increase value
of NC in parameter state-
ment.
NAPS VALUE OF 'X1
NAPP VALUE OF 'Y1
IS GREATER THAN
Reduce value of NAPS in
input or increase value
of NAPP in parameter
statement.
NCOM2+1 VALUE OF 'X1
NCMPTS VALUE OF 'Y1
IS GREATER THAN
Reduce value of NCOM2 in
input or increase value
of NCMPTS in parameter
statement.
NHORIZ VALUE OF 'X'
NCMPTS VALUE OF 'Y1
IS GREATER THAN
Reduce value of NHORIZ in
input or increase value.
of NCMPTS in parameter
statement.
INITL
NPLOTS VALUE OF 'X1 IS GREATER THAN
7
FATAL ERROR: SOIL PROFILE DESCRIP-
TION IS INCOMPLETE, INFORMATION
READ IN FOR ONLY 'X' OF 'Y1 CM
Reduce values of NPLOTS
below 7 in input.
Reduce the value of CORED
or increase the thickness
of horizons so that they
are greater than or equal
to CORED.
FATAL ERROR: SUM OF HORIZON THICK-
NESS EXCEEDS CORE DEPTH
Reduce the value of NHORIZ
or increase CORED so that
sum of horizon thickness
equals core depth.
SUSGPO: 1984—559-111/10745
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